Time Series & Recurrent Neural Networks

ACTL3143 & ACTL5111 Deep Learning for Actuaries

Patrick Laub

Time Series

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Tabular data vs time series data

Tabular data

We have a dataset \{ \boldsymbol{x}_i, y_i \}_{i=1}^n which we assume are i.i.d. observations.

Brand	Mileage	# Claims
BMW	101 km	1
Audi	432 km	0
Volvo	3 km	5
\vdots	\vdots	\vdots

The goal is to predict the y for some covariates \boldsymbol{x}.

Time series data

Have a sequence \{ \boldsymbol{x}_t, y_t \}_{t=1}^T of observations taken at regular time intervals.

Date	Humidity	Temp.
Jan 1	60%	20 °C
Jan 2	65%	22 °C
Jan 3	70%	21 °C
\vdots	\vdots	\vdots

The task is to forecast future values based on the past.

Attributes of time series data

• Temporal ordering: The order of the observations matters.
• Trend: The general direction of the data.
• Noise: Random fluctuations in the data.
• Seasonality: Patterns that repeat at regular intervals.

Note

Question: What will be the temperature in Berlin tomorrow? What information would you use to make a prediction?

Australian financial stocks

# First, install yfinance if you haven't already:
# pip install yfinance
import yfinance as yf
import pandas as pd
from datetime import date

# 1. Define the ASX tickers (Yahoo uses “.AX” for Australian stocks
#    and “^AXJO” for the S&P/ASX 200 index)
tickers = {
    "ANZ":    "ANZ.AX",
    "BOQ":    "BOQ.AX",
    "CBA":    "CBA.AX",
    "NAB":    "NAB.AX",
    "QBE":    "QBE.AX",
    "SUN":    "SUN.AX",
    "WBC":    "WBC.AX",
    "ASX200": "^AXJO"
}

# 2. Choose your date range
start = "1999-01-01"
end   = date.today().isoformat()  # e.g. "2025-06-29"

# 3. Download the data
#    This returns a Panel-like DataFrame: columns are tickers, rows are trading dates.
raw_data = yf.download(
    tickers=list(tickers.values()),
    start=start,
    end=end,
    progress=False
)

raw_data.to_csv("asx_raw_data.csv")  # Optional: Save raw data for inspection

data = raw_data["Close"]

# 4. Rename columns to the simple names
data.rename(columns={v: k for k, v in tickers.items()}, inplace=True)

cols = ["ANZ", "ASX200", "BOQ", "CBA", "NAB", "QBE", "SUN", "WBC"]
data = data[cols]

# 5. (Optional) Inspect the first few rows
print(data.head())

# 6. Save to CSV
output_path = "asx_daily_close_prices.csv"
data.to_csv(output_path)
print(f"Saved daily close prices to {output_path}")

Australian financial stocks

stocks = pd.read_csv("aus_fin_stocks.csv")
stocks

	Date	ANZ	ASX200	BOQ	CBA	NAB	QBE	SUN	WBC
0	1999-01-01	8.413491	NaN	NaN	14.560169	NaN	NaN	7.965791	NaN
1	1999-01-04	8.476515	2732.199951	NaN	14.402927	12.995510	2.648447	7.965791	6.370629
2	1999-01-05	8.452881	2716.600098	NaN	14.409224	13.169948	2.564247	7.965791	6.485921
...	...	...	...	...	...	...	...	...	...
6742	2025-06-25	29.100000	8559.200195	7.86	191.399994	40.049999	23.480000	21.750000	34.540001
6743	2025-06-26	29.740000	8550.799805	7.86	190.710007	39.889999	23.330000	21.459999	34.570000
6744	2025-06-27	29.200001	8514.200195	7.77	185.360001	39.259998	23.219999	21.330000	33.900002

6745 rows × 9 columns

Plot

stocks.plot()

Data types and NA values

stocks.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6745 entries, 0 to 6744
Data columns (total 9 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Date    6745 non-null   object 
 1   ANZ     6745 non-null   float64
 2   ASX200  6691 non-null   float64
 3   BOQ     6599 non-null   float64
 4   CBA     6745 non-null   float64
 5   NAB     6744 non-null   float64
 6   QBE     6744 non-null   float64
 7   SUN     6745 non-null   float64
 8   WBC     6744 non-null   float64
dtypes: float64(8), object(1)
memory usage: 474.4+ KB

for col in stocks.columns:
    print(f"{col}: {stocks[col].isna().sum()}")

Date: 0
ANZ: 0
ASX200: 54
BOQ: 146
CBA: 0
NAB: 1
QBE: 1
SUN: 0
WBC: 1

asx200 = stocks.pop("ASX200")

Set the index to the date

stocks["Date"] = pd.to_datetime(stocks["Date"])
stocks = stocks.set_index("Date") # or `stocks.set_index("Date", inplace=True)`
stocks

	ANZ	BOQ	CBA	NAB	QBE	SUN	WBC
Date
1999-01-01	8.413491	NaN	14.560169	NaN	NaN	7.965791	NaN
1999-01-04	8.476515	NaN	14.402927	12.995510	2.648447	7.965791	6.370629
1999-01-05	8.452881	NaN	14.409224	13.169948	2.564247	7.965791	6.485921
...	...	...	...	...	...	...	...
2025-06-25	29.100000	7.86	191.399994	40.049999	23.480000	21.750000	34.540001
2025-06-26	29.740000	7.86	190.710007	39.889999	23.330000	21.459999	34.570000
2025-06-27	29.200001	7.77	185.360001	39.259998	23.219999	21.330000	33.900002

6745 rows × 7 columns

Plot II

stocks.plot()
plt.ylabel("Stock Price ($)")
plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.5), ncol=4);

Can index using dates I

stocks.loc["2010-1-4":"2010-01-8"]

	ANZ	BOQ	CBA	NAB	QBE	SUN	WBC
Date
2010-01-04	18.874071	8.412694	34.510429	14.440043	13.831507	10.478957	14.958415
2010-01-05	18.964771	8.520640	35.032455	14.624498	13.902833	10.636262	15.082577
2010-01-06	18.684425	8.477461	35.208561	14.339908	13.688861	10.406354	15.011630
2010-01-07	18.239164	8.218388	34.868923	14.223969	13.441965	10.551559	14.810603
2010-01-08	18.346357	8.419890	35.321777	14.176538	13.590102	10.612061	14.869730

Note, these ranges are inclusive, not like Python’s normal slicing.

Can index using dates II

So to get 2019’s December and all of 2020 for CBA:

stocks.loc["2019-12":"2020", ["CBA"]]

	CBA
Date
2019-12-02	66.193298
2019-12-03	64.494362
2019-12-04	63.250660
...	...
2020-12-29	70.822792
2020-12-30	70.468712
2020-12-31	69.221031

275 rows × 1 columns

Can look at the first differences

stocks.diff().plot()
plt.ylabel("Daily Price Changes ($)")
plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.5), ncol=4);

Can look at the percentage changes

stocks.pct_change().plot()
plt.ylabel("Daily Returns (%)")
plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.5), ncol=4);

Focus on one stock

stock = stocks[["CBA"]].copy()
stock

	CBA
Date
1999-01-01	14.560169
1999-01-04	14.402927
1999-01-05	14.409224
...	...
2025-06-25	191.399994
2025-06-26	190.710007
2025-06-27	185.360001

6745 rows × 1 columns

stock.plot()
plt.ylabel("Stock Price ($)");

stock.isna().sum()

CBA    0
dtype: int64

Convert to log returns

Instead of working with raw prices, we’ll work with daily log returns:

# Calculate log returns
stock_log = np.log(stock / stock.shift(1)).dropna()
stock_log.head()

	CBA
Date
1999-01-04	-0.010858
1999-01-05	0.000437
1999-01-06	0.008042
1999-01-07	0.025859
1999-01-08	-0.021323

stock_log.plot()
plt.ylabel("Daily Log Return")
plt.title("CBA Daily Log Returns");

# Distribution of log returns
stock_log.hist(bins=50, alpha=0.7)
plt.xlabel("Daily Log Return")
plt.ylabel("Frequency")
plt.title("Distribution of CBA Daily Log Returns");

Fill in the missing values

asx200 = pd.DataFrame(asx200).set_index(stocks.index)
missing_day = asx200.index[asx200["ASX200"].isna()][1]
prev_day = missing_day - pd.Timedelta(days=1)
after = missing_day + pd.Timedelta(days=3)

asx200.loc[prev_day:after]

	ASX200
Date
1999-01-25	2713.100098
1999-01-26	NaN
1999-01-27	2738.800049
1999-01-28	2765.100098
1999-01-29	2781.699951

asx200 = asx200.ffill()
asx200.loc[prev_day:after]

	ASX200
Date
1999-01-25	2713.100098
1999-01-26	2713.100098
1999-01-27	2738.800049
1999-01-28	2765.100098
1999-01-29	2781.699951

Baseline forecasts

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Helper functions

def log_to_price(log_returns, initial_price):
    """Convert log returns to raw prices given an initial price."""
    # Use cumulative sum of log returns for numerical stability
    # P_t = P_0 * exp(sum of log returns from 1 to t)
    cumulative_log_returns = log_returns.cumsum()
    prices = initial_price * np.exp(cumulative_log_returns)
    
    return prices

def get_last_price(stock_df, cutoff_date):
    """Get the last known price before the forecast period starts."""
    last_known_date = stock_df.loc[:cutoff_date].index[-1]
    return stock_df.loc[last_known_date, "CBA"]

Persistence forecast

Predict the next value to be the same as the current value.

last_price = get_last_price(stock, cutoff_date="2024-12")
log_persistence = pd.Series(0.0, index=stock_log.loc["2025":].index)
persistence_prices = log_to_price(log_persistence, last_price)

start = "2024-12"
end = "2025"
stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.plot(label="Persistence")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend()

Extrapolate the trend

recent_log_returns = stock_log.loc["2022-01":"2024-12"]
trend_log = recent_log_returns.mean().values[0]
print(f"Average daily log return: {trend_log:.6f}")

Average daily log return: 0.000709

log_trend = pd.Series(trend_log, index=stock_log.loc["2025":].index)
trend_prices = log_to_price(log_trend, last_price)

# Plot the trend line over the top of the 'recent_log_returns' data
recent_log_returns.plot()
plt.axhline(trend_log, color="red", linestyle="--", label="Trend (mean log return)")
plt.ylabel("Daily Log Return");

Trend fitted

# Create trend forecast for the recent period to show fitted trend
recent_trend_log = pd.Series(trend_log, index=recent_log_returns.index)
trend_start_price = get_last_price(stock, cutoff_date=recent_log_returns.index[0].strftime('%Y-%m-%d'))
recent_trend_prices = log_to_price(recent_trend_log, trend_start_price)

stock.loc[recent_log_returns.index, ["CBA"]].plot(label="CBA")
recent_trend_prices.plot(label="Trend")
plt.axvline(recent_log_returns.index[0], color="gray", linestyle=":", linewidth=1)
plt.axvline(recent_log_returns.index[-1], color="gray", linestyle=":", linewidth=1)
plt.ylabel("Stock Price ($)")
plt.legend();

Trend forecasts

stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.loc[start:end].plot(label="Persistence")
trend_prices.loc[start:end].plot(label="Trend")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(ncol=3, loc="upper center", bbox_to_anchor=(0.5, 1.3));

Which is better?

If we look at the mean squared error (MSE) of the two models:

# Calculate MSE using the actual forecasts we computed
actual_prices = stock.loc["2025":, "CBA"]
persistence_mse = mean_squared_error(actual_prices, persistence_prices)
trend_mse = mean_squared_error(actual_prices, trend_prices)
persistence_mse, trend_mse

(254.04075100411256, 99.28717915684173)

Use the history

cba_log_shifted = stock_log["CBA"].head().shift(1)
both = pd.concat([stock_log["CBA"].head(), cba_log_shifted], axis=1, keys=["Today", "Yesterday"])
both

	Today	Yesterday
Date
1999-01-04	-0.010858	NaN
1999-01-05	0.000437	-0.010858
1999-01-06	0.008042	0.000437
1999-01-07	0.025859	0.008042
1999-01-08	-0.021323	0.025859

def lagged_timeseries(df, target, window=40):
    lagged = pd.DataFrame()
    for i in range(window, 0, -1):
        lagged[f"T-{i}"] = df[target].shift(i)
    lagged["T"] = df[target].values
    return lagged

Lagged time series

df_lags = lagged_timeseries(stock_log, "CBA", 40)
df_lags

	T-40	T-39	T-38	T-37	T-36	T-35	T-34	T-33	T-32	T-31	...	T-9	T-8	T-7	T-6	T-5	T-4	T-3	T-2	T-1	T
Date
1999-01-04	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-0.010858
1999-01-05	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-0.010858	0.000437
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2025-06-26	0.021968	0.003894	0.014307	-0.016222	-0.001139	-0.004689	-0.002715	0.009202	0.000838	-0.006240	...	-0.006558	0.000334	-0.001506	0.005789	0.014710	-0.001752	0.009922	0.020297	0.017232	-0.003611
2025-06-27	0.003894	0.014307	-0.016222	-0.001139	-0.004689	-0.002715	0.009202	0.000838	-0.006240	0.008153	...	0.000334	-0.001506	0.005789	0.014710	-0.001752	0.009922	0.020297	0.017232	-0.003611	-0.028454

6744 rows × 41 columns

Split into training and testing

# Split the data in time
X_train = df_lags.loc[:"2021"]
X_val = df_lags.loc["2022-01":"2024-12"]  # 2022-2024
X_test = df_lags.loc["2025":]  # 2025

# Remove any with NAs and split into X and y
X_train = X_train.dropna()
X_val = X_val.dropna()
X_test = X_test.dropna()

y_train = X_train.pop("T")
y_val = X_val.pop("T")
y_test = X_test.pop("T")

X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape

((5825, 40), (5825,), (757, 40), (757,), (122, 40), (122,))

Inspect the split data

X_train

	T-40	T-39	T-38	T-37	T-36	T-35	T-34	T-33	T-32	T-31	...	T-10	T-9	T-8	T-7	T-6	T-5	T-4	T-3	T-2	T-1
Date
1999-03-01	-0.010858	0.000437	0.008042	0.025859	-0.021323	-0.010573	-0.011214	-0.014823	-0.009704	0.018119	...	0.007118	-0.026875	0.000121	-0.008127	0.000000	-0.009016	0.012275	0.010156	-0.014231	-0.011912
1999-03-02	0.000437	0.008042	0.025859	-0.021323	-0.010573	-0.011214	-0.014823	-0.009704	0.018119	0.012994	...	-0.026875	0.000121	-0.008127	0.000000	-0.009016	0.012275	0.010156	-0.014231	-0.011912	0.010277
1999-03-03	0.008042	0.025859	-0.021323	-0.010573	-0.011214	-0.014823	-0.009704	0.018119	0.012994	0.010881	...	0.000121	-0.008127	0.000000	-0.009016	0.012275	0.010156	-0.014231	-0.011912	0.010277	0.004082
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-12-29	0.015263	-0.004999	0.011656	0.013921	0.011090	0.003821	-0.012058	0.000919	-0.016106	0.008825	...	0.001327	-0.005011	-0.006273	-0.001445	0.023788	0.000807	0.003924	0.001906	0.003302	0.005181
2021-12-30	-0.004999	0.011656	0.013921	0.011090	0.003821	-0.012058	0.000919	-0.016106	0.008825	-0.001018	...	-0.005011	-0.006273	-0.001445	0.023788	0.000807	0.003924	0.001906	0.003302	0.005181	0.012541
2021-12-31	0.011656	0.013921	0.011090	0.003821	-0.012058	0.000919	-0.016106	0.008825	-0.001018	-0.003060	...	-0.006273	-0.001445	0.023788	0.000807	0.003924	0.001906	0.003302	0.005181	0.012541	0.003624

5825 rows × 40 columns

Plot the split

y_train.plot()
y_val.plot()
y_test.plot()
plt.ylabel("Daily Log Return")
plt.legend(["Train", "Validation", "Test"], loc="center left", bbox_to_anchor=(1, 0.5));

Fit a linear model

lr = LinearRegression()
lr.fit(X_train, y_train);

Make a forecast for the test data:

y_pred = lr.predict(X_test)

Plot predictions in log return space

log_forecasts_df = pd.DataFrame({
    "Actual": y_test,
    "Linear": y_pred
}, index=y_test.index)

log_forecasts_df.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Convert to raw prices and plot

linear_log_forecasts = pd.Series(y_pred, index=y_test.index)
prev_price = stock.shift(1).loc["2025", "CBA"]
linear_price_forecasts = prev_price * np.exp(linear_log_forecasts)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.reindex(stock.loc[start:end].index).plot(label="Persistence")
trend_prices.reindex(stock.loc[start:end].index).plot(label="Trend")
linear_price_forecasts.reindex(stock.loc[start:end].index).plot(label="Linear")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Distribution of log return predictions

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))

# Actual log returns
y_val.hist(bins=30, alpha=0.7, ax=ax1, label="Actual")
ax1.set_xlabel("Daily Log Return")
ax1.set_ylabel("Frequency")
ax1.set_title("Actual Log Returns")

# Predicted log returns
pd.Series(y_pred).hist(bins=30, alpha=0.7, ax=ax2, label="Predicted", color="orange")
ax2.set_xlabel("Daily Log Return")
ax2.set_ylabel("Frequency")
ax2.set_title("Predicted Log Returns")

plt.tight_layout()

# Calculate MSE on raw prices using explicit forecast calculation
test_actual_prices = stock.loc["2025":, "CBA"]
test_linear_prices = linear_price_forecasts.loc["2025":]
linear_mse = mean_squared_error(test_actual_prices, test_linear_prices)
linear_mse

5.136664038344952

persistence_mse, trend_mse, linear_mse

(254.04075100411256, 99.28717915684173, 5.136664038344952)

Multi-step forecasts

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Comparing apples to apples

The linear model is only producing one-step-ahead forecasts.

The other models are producing multi-step-ahead forecasts.

shifted_forecast = stock["CBA"].shift(1).loc["2025":]

stock.loc[start:end, ["CBA"]].plot(label="CBA")
linear_price_forecasts.reindex(stock.loc[start:end].index).plot(label="Linear")
shifted_forecast.reindex(stock.loc[start:end].index).plot(label="Shifted")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

shifted_mse = mean_squared_error(stock.loc["2025":, "CBA"], shifted_forecast)
persistence_mse, trend_mse, linear_mse, shifted_mse

(254.04075100411256, 99.28717915684173, 5.136664038344952, 5.06190042634739)

Autoregressive forecasts

The linear model needs the last 90 days to make a forecast.

Idea: Make the first forecast, then use that to make the next forecast, and so on.

\begin{aligned} \hat{y}_t &= \beta_0 + \beta_1 y_{t-1} + \beta_2 y_{t-2} + \ldots + \beta_n y_{t-n} \\ \hat{y}_{t+1} &= \beta_0 + \beta_1 \hat{y}_t + \beta_2 y_{t-1} + \ldots + \beta_n y_{t-n+1} \\ \hat{y}_{t+2} &= \beta_0 + \beta_1 \hat{y}_{t+1} + \beta_2 \hat{y}_t + \ldots + \beta_n y_{t-n+2} \end{aligned} \vdots \hat{y}_{t+k} = \beta_0 + \beta_1 \hat{y}_{t+k-1} + \beta_2 \hat{y}_{t+k-2} + \ldots + \beta_n \hat{y}_{t+k-n}

Autoregressive forecasting function

def autoregressive_forecast(model, X_val, suppress=False):
    """
    Generate a multi-step forecast using the given model.
    """
    multi_step = pd.Series(index=X_val.index, name="Multi Step")

    # Initialize the input data for forecasting
    input_data = X_val.iloc[0].values.reshape(1, -1)

    for i in range(len(multi_step)):
        # Ensure input_data has the correct feature names
        input_df = pd.DataFrame(input_data, columns=X_val.columns)
        if suppress:
            next_value = model.predict(input_df, verbose=0)
        else:
            next_value = model.predict(input_df) 

        multi_step.iloc[i] = next_value

        # Append that prediction to the input for the next forecast
        if i + 1 < len(multi_step):
            input_data = np.append(input_data[:, 1:], next_value).reshape(1, -1)

    return multi_step

Look at the autoregressive linear forecasts

lr_forecast = autoregressive_forecast(lr, X_test)
lr_multi_step_prices = log_to_price(lr_forecast, last_price)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.reindex(stock.loc[start:end].index).plot(label="Persistence")
trend_prices.reindex(stock.loc[start:end].index).plot(label="Trend")
lr_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS Linear")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Compare log return forecasts

log_forecasts_multistep = pd.DataFrame({
    "Actual": y_test,
    "One-step Linear": y_pred,
    "Multi-step Linear": lr_forecast
}, index=y_test.index)

log_forecasts_multistep.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Metrics

One-step-ahead forecasts:

linear_mse, shifted_mse

(5.136664038344952, 5.06190042634739)

Multi-step-ahead forecasts:

multi_step_linear_mse = mean_squared_error(stock.loc["2025":, "CBA"], lr_multi_step_prices.loc["2025":])
persistence_mse, trend_mse, multi_step_linear_mse

(254.04075100411256, 99.28717915684173, 181.11397713761005)

Prefer only short windows

stock.loc["2025-01":"2025-01", ["CBA"]].plot(label="CBA")
persistence_prices.loc["2025-01":"2025-01"].plot(label="Persistence")
trend_prices.loc["2025-01":"2025-01"].plot(label="Trend")
lr_multi_step_prices.loc["2025-01":"2025-01"].plot(label="MS Linear")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

“It’s tough to make predictions, especially about the future.”

Neural network forecasts

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Simple feedforward neural network

model = Sequential([
        Rescaling(1/0.02),
        Dense(32, activation="leaky_relu"),
        Dense(1)])  # Linear activation for log returns
model.compile(optimizer="adam", loss="mean_absolute_error")

if Path("aus_fin_fnn_model.keras").exists():
    model = keras.models.load_model("aus_fin_fnn_model.keras")
else:
    es = EarlyStopping(patience=15, restore_best_weights=True)
    model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=500,
        callbacks=[es], verbose=0)
    model.save("aus_fin_fnn_model.keras")

model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ rescaling (Rescaling)           │ (None, 40)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 32)             │         1,312 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 1)              │            33 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 4,037 (15.77 KB)

 Trainable params: 1,345 (5.25 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 2,692 (10.52 KB)

Forecast and plot

y_pred = model.predict(X_test, verbose=0)

# Show log return predictions
log_forecasts_nn = pd.DataFrame({
    "Actual": y_test,
    "Linear": lr.predict(X_test),
    "FNN": y_pred.flatten()
}, index=y_test.index)

log_forecasts_nn.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Plot forecasts in raw price space

fnn_log_forecasts = pd.Series(y_pred.flatten(), index=y_test.index)
fnn_price_forecasts = prev_price * np.exp(fnn_log_forecasts)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
linear_price_forecasts.reindex(stock.loc[start:end].index).plot(label="Linear")
fnn_price_forecasts.reindex(stock.loc[start:end].index).plot(label="FNN")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Distribution of log return predictions

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))

# Actual log returns
y_test.hist(bins=30, alpha=0.7, ax=ax1, label="Actual")
ax1.set_xlabel("Daily Log Return")
ax1.set_ylabel("Frequency")
ax1.set_title("Actual Log Returns")

# Predicted log returns
pd.Series(y_pred.flatten()).hist(bins=30, alpha=0.7, ax=ax2, label="Predicted", color="orange")
ax2.set_xlabel("Daily Log Return")
ax2.set_ylabel("Frequency")
ax2.set_title("Predicted Log Returns")

plt.tight_layout()

Autoregressive forecasts

fnn_forecast = autoregressive_forecast(model, X_test, True)
fnn_multi_step_prices = log_to_price(fnn_forecast, last_price)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.reindex(stock.loc[start:end].index).plot(label="Persistence")
trend_prices.reindex(stock.loc[start:end].index).plot(label="Trend")
lr_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS Linear")
fnn_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS FNN")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Compare multi-step log return forecasts

log_forecasts_multistep_nn = pd.DataFrame({
    "Actual": y_test,
    "Multi-step Linear": lr_forecast,
    "Multi-step FNN": fnn_forecast
}, index=y_test.index)

log_forecasts_multistep_nn.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Metrics

One-step-ahead forecasts (MSE on raw prices):

nn_mse = mean_squared_error(stock.loc["2025":, "CBA"], fnn_price_forecasts.loc["2025":])
linear_mse, nn_mse

(5.136664038344952, 5.499976874450013)

Multi-step-ahead forecasts (MSE on raw prices):

multi_step_fnn_mse = mean_squared_error(stock.loc["2025":, "CBA"], fnn_multi_step_prices.loc["2025":])
persistence_mse, trend_mse, multi_step_linear_mse, multi_step_fnn_mse

(254.04075100411256, 99.28717915684173, 181.11397713761005, 216.57446624903855)

Recurrent Neural Networks

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Basic facts of RNNs

• A recurrent neural network is a type of neural network that is designed to process sequences of data (e.g. time series, sentences).
• A recurrent neural network is any network that contains a recurrent layer.
• A recurrent layer is a layer that processes data in a sequence.
• An RNN can have one or more recurrent layers.
• Weights are shared over time; this allows the model to be used on arbitrary-length sequences.

Applications

• Forecasting: revenue forecast, weather forecast, predict disease rate from medical history, etc.
• Classification: given a time series of the activities of a visitor on a website, classify whether the visitor is a bot or a human.
• Event detection: given a continuous data stream, identify the occurrence of a specific event. Example: Detect utterances like “Hey Alexa” from an audio stream.
• Anomaly detection: given a continuous data stream, detect anything unusual happening. Example: Detect unusual activity on the corporate network.

Origin of the name of RNNs

A recurrence relation is an equation that expresses each element of a sequence as a function of the preceding ones. More precisely, in the case where only the immediately preceding element is involved, a recurrence relation has the form

u_n = \psi(n, u_{n-1}) \quad \text{ for } \quad n > 0.

Example: Factorial n! = n (n-1)! for n > 0 given 0! = 1.

Diagram of an RNN cell

The RNN processes each data in the sequence one by one, while keeping memory of what came before.

Schematic of a recurrent neural network. E.g. SimpleRNN, LSTM, or GRU.

A SimpleRNN cell

Diagram of a SimpleRNN cell.

All the outputs before the final one are often discarded.

LSTM internals

GRU internals

Diagram of a GRU cell.

Stock prediction with recurrent networks

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

SimpleRNN

from keras.layers import SimpleRNN, Reshape
model = Sequential([
        Rescaling(1/0.02),
        Reshape((-1, 1)),
        SimpleRNN(64, activation="tanh"),
        Dense(1)])  # Linear activation for log returns
model.compile(optimizer="adam", loss="mean_absolute_error")

es = EarlyStopping(patience=15, restore_best_weights=True)
model.fit(X_train, y_train, validation_data=(X_val, y_val),
    epochs=500, callbacks=[es], verbose=0)
model.summary()

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ rescaling_1 (Rescaling)         │ (None, 40)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ reshape (Reshape)               │ (None, 40, 1)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ simple_rnn (SimpleRNN)          │ (None, 64)             │         4,224 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            65 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 12,869 (50.27 KB)

 Trainable params: 4,289 (16.75 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 8,580 (33.52 KB)

Forecast and plot

y_pred = model.predict(X_test.to_numpy(), verbose=0)

# Show log return predictions
log_forecasts_rnn = pd.DataFrame({
    "Actual": y_test,
    "FNN": fnn_forecast.values[:len(y_test)],  # Use the FNN forecast from previous section
    "SimpleRNN": y_pred.flatten()
}, index=y_test.index)

log_forecasts_rnn.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Plot forecasts in raw price space

rnn_log_forecasts = pd.Series(y_pred.flatten(), index=y_test.index)
rnn_price_forecasts = prev_price * np.exp(rnn_log_forecasts)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
linear_price_forecasts.reindex(stock.loc[start:end].index).plot(label="Linear")
fnn_price_forecasts.reindex(stock.loc[start:end].index).plot(label="FNN")
rnn_price_forecasts.reindex(stock.loc[start:end].index).plot(label="SimpleRNN")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Multi-step forecasts

rnn_forecast = autoregressive_forecast(model, X_test, True)
rnn_multi_step_prices = log_to_price(rnn_forecast, last_price)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.reindex(stock.loc[start:end].index).plot(label="Persistence")
trend_prices.reindex(stock.loc[start:end].index).plot(label="Trend")
lr_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS Linear")
fnn_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS FNN")
rnn_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS RNN")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Metrics

One-step-ahead forecasts (MSE on raw prices):

rnn_mse = mean_squared_error(stock.loc["2025":, "CBA"], rnn_price_forecasts.loc["2025":])
linear_mse, nn_mse, rnn_mse

(5.136664038344952, 5.499976874450013, 4.9401398570164075)

Multi-step-ahead forecasts (MSE on raw prices):

multi_step_rnn_mse = mean_squared_error(stock.loc["2025":, "CBA"], rnn_multi_step_prices.loc["2025":])
persistence_mse, trend_mse, multi_step_linear_mse, multi_step_fnn_mse, multi_step_rnn_mse

(254.04075100411256,
 99.28717915684173,
 181.11397713761005,
 216.57446624903855,
 112.51089934559508)

GRU

from keras.layers import GRU

model = Sequential([
        Rescaling(1/0.02),
        Reshape((-1, 1)),
        GRU(16, activation="tanh"),
        Dense(1)])  # Linear activation for log returns
model.compile(optimizer="adam", loss="mean_absolute_error")

es = EarlyStopping(patience=15, restore_best_weights=True)
model.fit(X_train, y_train, validation_data=(X_val, y_val),
    epochs=500, callbacks=[es], verbose=0)
model.summary()

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ rescaling_2 (Rescaling)         │ (None, 40)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ reshape_1 (Reshape)             │ (None, 40, 1)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ gru (GRU)                       │ (None, 16)             │           912 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 1)              │            17 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 2,789 (10.90 KB)

 Trainable params: 929 (3.63 KB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 1,860 (7.27 KB)

Forecast and plot

y_pred = model.predict(X_test, verbose=0)

# Show log return predictions
log_forecasts_gru = pd.DataFrame({
    "Actual": y_test,
    "SimpleRNN": rnn_forecast.values[:len(y_test)],  # Use the RNN forecast from previous section
    "GRU": y_pred.flatten()
}, index=y_test.index)

log_forecasts_gru.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Plot forecasts in raw price space

gru_log_forecasts = pd.Series(y_pred.flatten(), index=y_test.index)
gru_price_forecasts = prev_price * np.exp(gru_log_forecasts)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
linear_price_forecasts.reindex(stock.loc[start:end].index).plot(label="Linear")
fnn_price_forecasts.reindex(stock.loc[start:end].index).plot(label="FNN")
rnn_price_forecasts.reindex(stock.loc[start:end].index).plot(label="SimpleRNN")
gru_price_forecasts.reindex(stock.loc[start:end].index).plot(label="GRU")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

Multi-step forecasts

gru_forecast = autoregressive_forecast(model, X_test, True)
gru_multi_step_prices = log_to_price(gru_forecast, last_price)

stock.loc[start:end, ["CBA"]].plot(label="CBA")
persistence_prices.reindex(stock.loc[start:end].index).plot(label="Persistence")
trend_prices.reindex(stock.loc[start:end].index).plot(label="Trend")
lr_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS Linear")
fnn_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS FNN")
rnn_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS RNN")
gru_multi_step_prices.reindex(stock.loc[start:end].index).plot(label="MS GRU")
plt.axvline("2025-01", color="black", linestyle="--")
plt.ylabel("Stock Price ($)")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5));

All multi-step log return forecasts

log_forecasts_all = pd.DataFrame({
    "Actual": y_test,
    "Linear": lr_forecast,
    "FNN": fnn_forecast,
    "SimpleRNN": rnn_forecast,
    "GRU": gru_forecast
}, index=y_test.index)

log_forecasts_all.plot()
plt.ylabel("Daily Log Return")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

Metrics

One-step-ahead forecasts (MSE on raw prices):

gru_mse = mean_squared_error(stock.loc["2025":, "CBA"], gru_price_forecasts.loc["2025":])
linear_mse, nn_mse, rnn_mse, gru_mse

(5.136664038344952, 5.499976874450013, 4.9401398570164075, 5.187925181803358)

Multi-step-ahead forecasts (MSE on raw prices):

multi_step_gru_mse = mean_squared_error(stock.loc["2025":, "CBA"], gru_multi_step_prices.loc["2025":])
persistence_mse, trend_mse, multi_step_linear_mse, multi_step_fnn_mse, multi_step_rnn_mse, multi_step_gru_mse

(254.04075100411256,
 99.28717915684173,
 181.11397713761005,
 216.57446624903855,
 112.51089934559508,
 97.04131912129455)

Summary of all model performance

One-step forecasts

	MSE
Shifted	5.061900
Linear	5.136664
FNN	5.499977
SimpleRNN	4.940140
GRU	5.187925

Multi-step forecasts

	MSE
Persistence	254.040751
Trend	99.287179
Linear	181.113977
FNN	216.574466
SimpleRNN	112.510899
GRU	97.041319

Internals of the SimpleRNN

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

The rank of a time series

Say we had n observations of a time series x_1, x_2, \dots, x_n.

This \boldsymbol{x} = (x_1, \dots, x_n) would have shape (n,) & rank 1.

If instead we had a batch of b time series’

\boldsymbol{X} = \begin{pmatrix} x_7 & x_8 & \dots & x_{7+n-1} \\ x_2 & x_3 & \dots & x_{2+n-1} \\ \vdots & \vdots & \ddots & \vdots \\ x_3 & x_4 & \dots & x_{3+n-1} \\ \end{pmatrix} \,,

the batch \boldsymbol{X} would have shape (b, n) & rank 2.

Multivariate time series

t	x	y
0	x_0	y_0
1	x_1	y_1
2	x_2	y_2
3	x_3	y_3

Say n observations of the m time series, would be a shape (n, m) matrix of rank 2.

In Keras, a batch of b of these time series has shape (b, n, m) and has rank 3.

Note

Use \boldsymbol{x}_t \in \mathbb{R}^{1 \times m} to denote the vector of all time series at time t. Here, \boldsymbol{x}_t = (x_t, y_t).

SimpleRNN

Say each prediction is a vector of size d, so \boldsymbol{y}_t \in \mathbb{R}^{1 \times d}.

Then the main equation of a SimpleRNN, given \boldsymbol{y}_0 = \boldsymbol{0}, is

\boldsymbol{y}_t = \psi\bigl( \boldsymbol{x}_t \boldsymbol{W}_x + \boldsymbol{y}_{t-1} \boldsymbol{W}_y + \boldsymbol{b} \bigr) .

Here, \begin{aligned} &\boldsymbol{x}_t \in \mathbb{R}^{1 \times m}, \boldsymbol{W}_x \in \mathbb{R}^{m \times d}, \\ &\boldsymbol{y}_{t-1} \in \mathbb{R}^{1 \times d}, \boldsymbol{W}_y \in \mathbb{R}^{d \times d}, \text{ and } \boldsymbol{b} \in \mathbb{R}^{d}. \end{aligned}

SimpleRNN (in batches)

Say we operate on batches of size b, then \boldsymbol{Y}_t \in \mathbb{R}^{b \times d}.

The main equation of a SimpleRNN, given \boldsymbol{Y}_0 = \boldsymbol{0}, is \boldsymbol{Y}_t = \psi\bigl( \boldsymbol{X}_t \boldsymbol{W}_x + \boldsymbol{Y}_{t-1} \boldsymbol{W}_y + \boldsymbol{b} \bigr) . Here, \begin{aligned} &\boldsymbol{X}_t \in \mathbb{R}^{b \times m}, \boldsymbol{W}_x \in \mathbb{R}^{m \times d}, \\ &\boldsymbol{Y}_{t-1} \in \mathbb{R}^{b \times d}, \boldsymbol{W}_y \in \mathbb{R}^{d \times d}, \text{ and } \boldsymbol{b} \in \mathbb{R}^{d}. \end{aligned}

Simple Keras demo

num_obs = 4
num_time_steps = 3
num_time_series = 2

X = (
    np.arange(num_obs * num_time_steps * num_time_series)
    .astype(np.float32)
    .reshape([num_obs, num_time_steps, num_time_series])
)

output_size = 1
y = np.array([0, 0, 1, 1])

X[:2]

array([[[ 0.,  1.],
        [ 2.,  3.],
        [ 4.,  5.]],

       [[ 6.,  7.],
        [ 8.,  9.],
        [10., 11.]]], dtype=float32)

X[2:]

array([[[12., 13.],
        [14., 15.],
        [16., 17.]],

       [[18., 19.],
        [20., 21.],
        [22., 23.]]], dtype=float32)

Keras’ SimpleRNN

As usual, the SimpleRNN is just a layer in Keras.

from keras.layers import SimpleRNN

random.seed(1234)
model = Sequential([SimpleRNN(output_size, activation="sigmoid")])
model.compile(loss="binary_crossentropy", metrics=["accuracy"])

hist = model.fit(X, y, epochs=500, verbose=False)
model.evaluate(X, y, verbose=False)

[3.1487133502960205, 0.5]

The predicted probabilities on the training set are:

model.predict(X, verbose=0)

array([[0.97],
       [1.  ],
       [1.  ],
       [1.  ]], dtype=float32)

SimpleRNN weights

model.get_weights()

[array([[0.68],
        [0.2 ]], dtype=float32),
 array([[0.49]], dtype=float32),
 array([-0.51], dtype=float32)]

def sigmoid(x):
    return 1 / (1 + np.exp(-x))


W_x, W_y, b = model.get_weights()

Y = np.zeros((num_obs, output_size), dtype=np.float32)
for t in range(num_time_steps):
    X_t = X[:, t, :]
    z = X_t @ W_x + Y @ W_y + b
    Y = sigmoid(z)

Y

array([[0.97],
       [1.  ],
       [1.  ],
       [1.  ]], dtype=float32)

Other recurrent network variants

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Input and output sequences

Categories of recurrent neural networks: sequence to sequence, sequence to vector, vector to sequence, encoder-decoder network.

Input and output sequences

• Sequence to sequence: Useful for predicting time series such as using prices over the last N days to output the prices shifted one day into the future (i.e. from N-1 days ago to tomorrow.)
• Sequence to vector: ignore all outputs in the previous time steps except for the last one. Example: give a sentiment score to a sequence of words corresponding to a movie review.

Input and output sequences

• Vector to sequence: feed the network the same input vector over and over at each time step and let it output a sequence. Example: given that the input is an image, find a caption for it. The image is treated as an input vector (pixels in an image do not follow a sequence). The caption is a sequence of textual description of the image. A dataset containing images and their descriptions is the input of the RNN.
• The Encoder-Decoder: The encoder is a sequence-to-vector network. The decoder is a vector-to-sequence network. Example: Feed the network a sequence in one language. Use the encoder to convert the sentence into a single vector representation. The decoder decodes this vector into the translation of the sentence in another language.

Recurrent layers can be stacked.

Deep RNN unrolled through time.

CoreLogic Hedonic Home Value Index

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Australian House Price Indices

Note

I apologise in advance for not being able to share this dataset with anyone (it is not mine to share).

Percentage changes

changes = house_prices.pct_change().dropna()
changes.round(2)

	Brisbane	East_Bris	North_Bris	West_Bris	Melbourne	North_Syd	Sydney
Date
1990-02-28	0.03	-0.01	0.01	0.01	0.00	-0.00	-0.02
1990-03-31	0.01	0.03	0.01	0.01	0.02	-0.00	0.03
1990-04-30	0.02	0.02	0.01	-0.00	0.01	0.03	0.04
...	...	...	...	...	...	...	...
2021-03-31	0.04	0.04	0.03	0.04	0.02	0.05	0.05
2021-04-30	0.03	0.01	0.01	-0.00	0.01	0.02	0.02
2021-05-31	0.03	0.03	0.03	0.03	0.03	0.02	0.04

376 rows × 7 columns

Percentage changes

changes.plot();

The size of the changes

changes.mean()

Brisbane      0.005496
East_Bris     0.005416
North_Bris    0.005024
West_Bris     0.004842
Melbourne     0.005677
North_Syd     0.004819
Sydney        0.005526
dtype: float64

changes *= 100

changes.mean()

Brisbane      0.549605
East_Bris     0.541562
North_Bris    0.502390
West_Bris     0.484204
Melbourne     0.567700
North_Syd     0.481863
Sydney        0.552641
dtype: float64

changes.plot(legend=False);

Split without shuffling

num_train = int(0.6 * len(changes))
num_val = int(0.2 * len(changes))
num_test = len(changes) - num_train - num_val
print(f"# Train: {num_train}, # Val: {num_val}, # Test: {num_test}")

# Train: 225, # Val: 75, # Test: 76

Subsequences of a time series

Keras has a built-in method for converting a time series into subsequences/chunks.

from keras.utils import timeseries_dataset_from_array

integers = range(10)
dummy_dataset = timeseries_dataset_from_array(
    data=integers[:-3],
    targets=integers[3:],
    sequence_length=3,
    batch_size=2,
)

for inputs, targets in dummy_dataset:
    for i in range(inputs.shape[0]):
        print([int(x) for x in inputs[i]], int(targets[i]))

[0, 1, 2] 3
[1, 2, 3] 4
[2, 3, 4] 5
[3, 4, 5] 6
[4, 5, 6] 7

Predicting Sydney House Prices

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Creating dataset objects

# Num. of input time series.
num_ts = changes.shape[1]

# How many prev. months to use.
seq_length = 6

# Predict the next month ahead.
ahead = 1

# The index of the first target.
delay = seq_length + ahead - 1

# Which suburb to predict.
target_suburb = changes["Sydney"]

train_ds = timeseries_dataset_from_array(
    changes[:-delay],
    targets=target_suburb[delay:],
    sequence_length=seq_length,
    end_index=num_train,
)

val_ds = timeseries_dataset_from_array(
    changes[:-delay],
    targets=target_suburb[delay:],
    sequence_length=seq_length,
    start_index=num_train,
    end_index=num_train + num_val,
)

test_ds = timeseries_dataset_from_array(
    changes[:-delay],
    targets=target_suburb[delay:],
    sequence_length=seq_length,
    start_index=num_train + num_val,
)

Converting Dataset to numpy

The Dataset object can be handed to Keras directly, but if we really need a numpy array, we can run:

X_train = np.concatenate(list(train_ds.map(lambda x, y: x)))
y_train = np.concatenate(list(train_ds.map(lambda x, y: y)))

The shape of our training set is now:

X_train.shape

(220, 6, 7)

y_train.shape

(220,)

Converting the rest to numpy arrays:

X_val = np.concatenate(list(val_ds.map(lambda x, y: x)))
y_val = np.concatenate(list(val_ds.map(lambda x, y: y)))
X_test = np.concatenate(list(test_ds.map(lambda x, y: x)))
y_test = np.concatenate(list(test_ds.map(lambda x, y: y)))

A dense network

from keras.layers import Input, Flatten
random.seed(1)
model_dense = Sequential([
    Input((seq_length, num_ts)),
    Flatten(),
    Dense(50, activation="leaky_relu"),
    Dense(20, activation="leaky_relu"),
    Dense(1, activation="linear")
])
model_dense.compile(loss="mse", optimizer="adam")
print(f"This model has {model_dense.count_params()} parameters.")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)
%time hist = model_dense.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0);

This model has 3191 parameters.
Epoch 57: early stopping
Restoring model weights from the end of the best epoch: 7.
CPU times: user 2.72 s, sys: 306 ms, total: 3.02 s
Wall time: 2.68 s

Plot the model

from keras.utils import plot_model

plot_model(model_dense, show_shapes=True)

Assess the fits

model_dense.evaluate(X_val, y_val, verbose=0)

1.1644607782363892

y_pred = model_dense.predict(X_val, verbose=0)
plt.plot(y_val, label="Sydney")
plt.plot(y_pred, label="Dense")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

A SimpleRNN layer

random.seed(1)

model_simple = Sequential([
    Input((seq_length, num_ts)),
    SimpleRNN(50),
    Dense(1, activation="linear")
])
model_simple.compile(loss="mse", optimizer="adam")
print(f"This model has {model_simple.count_params()} parameters.")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)
%time hist = model_simple.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0);

This model has 2951 parameters.
Epoch 62: early stopping
Restoring model weights from the end of the best epoch: 12.
CPU times: user 3.63 s, sys: 1.45 s, total: 5.08 s
Wall time: 3.37 s

Plot the model

plot_model(model_simple, show_shapes=True)

Assess the fits

model_simple.evaluate(X_val, y_val, verbose=0)

1.2507916688919067

y_pred = model_simple.predict(X_val, verbose=0)

plt.plot(y_val, label="Sydney")
plt.plot(y_pred, label="SimpleRNN")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

WARNING:tensorflow:5 out of the last 128 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x1725300e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:6 out of the last 130 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x1725300e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

A LSTM layer

from keras.layers import LSTM

random.seed(1)

model_lstm = Sequential([
    Input((seq_length, num_ts)),
    LSTM(50),
    Dense(1, activation="linear")
])

model_lstm.compile(loss="mse", optimizer="adam")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)

%time hist = model_lstm.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0);

Epoch 59: early stopping
Restoring model weights from the end of the best epoch: 9.
CPU times: user 3.75 s, sys: 1.12 s, total: 4.87 s
Wall time: 3.51 s

Assess the fits

model_lstm.evaluate(X_val, y_val, verbose=0)

0.8353261947631836

y_pred = model_lstm.predict(X_val, verbose=0)
plt.plot(y_val, label="Sydney")
plt.plot(y_pred, label="LSTM")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

A GRU layer

from keras.layers import GRU

random.seed(1)

model_gru = Sequential([
    Input((seq_length, num_ts)),
    GRU(50),
    Dense(1, activation="linear")
])

model_gru.compile(loss="mse", optimizer="adam")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)

%time hist = model_gru.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0)

Epoch 57: early stopping
Restoring model weights from the end of the best epoch: 7.
CPU times: user 3.84 s, sys: 367 ms, total: 4.21 s
Wall time: 3.51 s

Assess the fits

model_gru.evaluate(X_val, y_val, verbose=0)

0.7435100078582764

y_pred = model_gru.predict(X_val, verbose=0)
plt.plot(y_val, label="Sydney")
plt.plot(y_pred, label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

Two GRU layers

random.seed(1)

model_two_grus = Sequential([
    Input((seq_length, num_ts)),
    GRU(50, return_sequences=True),
    GRU(50),
    Dense(1, activation="linear")
])

model_two_grus.compile(loss="mse", optimizer="adam")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)

%time hist = model_two_grus.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0)

Epoch 56: early stopping
Restoring model weights from the end of the best epoch: 6.
CPU times: user 5.16 s, sys: 477 ms, total: 5.64 s
Wall time: 4.57 s

Assess the fits

model_two_grus.evaluate(X_val, y_val, verbose=0)

0.7989509105682373

y_pred = model_two_grus.predict(X_val, verbose=0)
plt.plot(y_val, label="Sydney")
plt.plot(y_pred, label="2 GRUs")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

Compare the models

	Model	MSE
1	SimpleRNN	1.250792
0	Dense	1.164461
2	LSTM	0.835326
4	2 GRUs	0.798951
3	GRU	0.743510

The network with two GRU layers is the best.

model_two_grus.evaluate(X_test, y_test, verbose=0)

1.855254888534546

Test set

y_pred = model_two_grus.predict(X_test, verbose=0)
plt.plot(y_test, label="Sydney")
plt.plot(y_pred, label="2 GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

Predicting Multiple Time Series

Lecture Outline

• Time Series

• Baseline forecasts

• Multi-step forecasts

• Neural network forecasts

• Recurrent Neural Networks

• Stock prediction with recurrent networks

• Internals of the SimpleRNN

• Other recurrent network variants

• CoreLogic Hedonic Home Value Index

• Predicting Sydney House Prices

• Predicting Multiple Time Series

Creating dataset objects

Change the targets argument to include all the suburbs.

train_ds = timeseries_dataset_from_array(
    changes[:-delay],
    targets=changes[delay:],
    sequence_length=seq_length,
    end_index=num_train,
)

val_ds = timeseries_dataset_from_array(
    changes[:-delay],
    targets=changes[delay:],
    sequence_length=seq_length,
    start_index=num_train,
    end_index=num_train + num_val,
)

test_ds = timeseries_dataset_from_array(
    changes[:-delay],
    targets=changes[delay:],
    sequence_length=seq_length,
    start_index=num_train + num_val,
)

Converting Dataset to numpy

The shape of our training set is now:

X_train = np.concatenate(list(train_ds.map(lambda x, y: x)))
X_train.shape

(220, 6, 7)

y_train = np.concatenate(list(train_ds.map(lambda x, y: y)))
y_train.shape

(220, 7)

Converting the rest to numpy arrays:

X_val = np.concatenate(list(val_ds.map(lambda x, y: x)))
y_val = np.concatenate(list(val_ds.map(lambda x, y: y)))
X_test = np.concatenate(list(test_ds.map(lambda x, y: x)))
y_test = np.concatenate(list(test_ds.map(lambda x, y: y)))

A dense network

random.seed(1)
model_dense = Sequential([
    Input((seq_length, num_ts)),
    Flatten(),
    Dense(50, activation="leaky_relu"),
    Dense(20, activation="leaky_relu"),
    Dense(num_ts, activation="linear")
])
model_dense.compile(loss="mse", optimizer="adam")
print(f"This model has {model_dense.count_params()} parameters.")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)
%time hist = model_dense.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0);

This model has 3317 parameters.
Epoch 75: early stopping
Restoring model weights from the end of the best epoch: 25.
CPU times: user 3.52 s, sys: 376 ms, total: 3.89 s
Wall time: 3.47 s

Plot the model

plot_model(model_dense, show_shapes=True)

Assess the fits

model_dense.evaluate(X_val, y_val, verbose=0)

1.4294650554656982

Y_pred = model_dense.predict(X_val, verbose=0)
plt.scatter(y_val, Y_pred)
add_diagonal_line()
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

plt.plot(y_val[:, 4], label="Melbourne")
plt.plot(Y_pred[:, 4], label="Dense")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

plt.plot(y_val[:, 0], label="Brisbane")
plt.plot(Y_pred[:, 0], label="Dense")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False)
plt.show()

plt.plot(y_val[:, 6], label="Sydney")
plt.plot(Y_pred[:, 6], label="Dense")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

A SimpleRNN layer

random.seed(1)

model_simple = Sequential([
    Input((seq_length, num_ts)),
    SimpleRNN(50),
    Dense(num_ts, activation="linear")
])
model_simple.compile(loss="mse", optimizer="adam")
print(f"This model has {model_simple.count_params()} parameters.")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)
%time hist = model_simple.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0);

This model has 3257 parameters.
Epoch 70: early stopping
Restoring model weights from the end of the best epoch: 20.
CPU times: user 4.12 s, sys: 395 ms, total: 4.51 s
Wall time: 3.63 s

Plot the model

plot_model(model_simple, show_shapes=True)

Assess the fits

model_simple.evaluate(X_val, y_val, verbose=0)

1.4916820526123047

Y_pred = model_simple.predict(X_val, verbose=0)
plt.scatter(y_val, Y_pred)
add_diagonal_line()
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

plt.plot(y_val[:, 4], label="Melbourne")
plt.plot(Y_pred[:, 4], label="SimpleRNN")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

plt.plot(y_val[:, 0], label="Brisbane")
plt.plot(Y_pred[:, 0], label="SimpleRNN")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False)
plt.show()

plt.plot(y_val[:, 6], label="Sydney")
plt.plot(Y_pred[:, 6], label="SimpleRNN")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

A LSTM layer

random.seed(1)

model_lstm = Sequential([
    Input((seq_length, num_ts)),
    LSTM(50),
    Dense(num_ts, activation="linear")
])

model_lstm.compile(loss="mse", optimizer="adam")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)

%time hist = model_lstm.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0);

Epoch 74: early stopping
Restoring model weights from the end of the best epoch: 24.
CPU times: user 4.96 s, sys: 491 ms, total: 5.45 s
Wall time: 4.49 s

Assess the fits

model_lstm.evaluate(X_val, y_val, verbose=0)

1.3311247825622559

Y_pred = model_lstm.predict(X_val, verbose=0)
plt.scatter(y_val, Y_pred)
add_diagonal_line()
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

plt.plot(y_val[:, 4], label="Melbourne")
plt.plot(Y_pred[:, 4], label="LSTM")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

plt.plot(y_val[:, 0], label="Brisbane")
plt.plot(Y_pred[:, 0], label="LSTM")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False)
plt.show()

plt.plot(y_val[:, 6], label="Sydney")
plt.plot(Y_pred[:, 6], label="LSTM")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

A GRU layer

random.seed(1)

model_gru = Sequential([
    Input((seq_length, num_ts)),
    GRU(50),
    Dense(num_ts, activation="linear")
])

model_gru.compile(loss="mse", optimizer="adam")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)

%time hist = model_gru.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0)

Epoch 70: early stopping
Restoring model weights from the end of the best epoch: 20.
CPU times: user 4.73 s, sys: 448 ms, total: 5.17 s
Wall time: 4.13 s

Assess the fits

model_gru.evaluate(X_val, y_val, verbose=0)

1.344503402709961

Y_pred = model_gru.predict(X_val, verbose=0)
plt.scatter(y_val, Y_pred)
add_diagonal_line()
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

plt.plot(y_val[:, 4], label="Melbourne")
plt.plot(Y_pred[:, 4], label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

plt.plot(y_val[:, 0], label="Brisbane")
plt.plot(Y_pred[:, 0], label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False)
plt.show()

plt.plot(y_val[:, 6], label="Sydney")
plt.plot(Y_pred[:, 6], label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

Two GRU layers

random.seed(1)

model_two_grus = Sequential([
    Input((seq_length, num_ts)),
    GRU(50, return_sequences=True),
    GRU(50),
    Dense(num_ts, activation="linear")
])

model_two_grus.compile(loss="mse", optimizer="adam")

es = EarlyStopping(patience=50, restore_best_weights=True, verbose=1)

%time hist = model_two_grus.fit(X_train, y_train, epochs=1_000, \
  validation_data=(X_val, y_val), callbacks=[es], verbose=0)

Epoch 67: early stopping
Restoring model weights from the end of the best epoch: 17.
CPU times: user 5.71 s, sys: 524 ms, total: 6.24 s
Wall time: 5.13 s

Assess the fits

model_two_grus.evaluate(X_val, y_val, verbose=0)

1.358651041984558

Y_pred = model_two_grus.predict(X_val, verbose=0)
plt.scatter(y_val, Y_pred)
add_diagonal_line()
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

plt.plot(y_val[:, 4], label="Melbourne")
plt.plot(Y_pred[:, 4], label="2 GRUs")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

plt.plot(y_val[:, 0], label="Brisbane")
plt.plot(Y_pred[:, 0], label="2 GRUs")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False)
plt.show()

plt.plot(y_val[:, 6], label="Sydney")
plt.plot(Y_pred[:, 6], label="2 GRUs")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

Compare the models

models = [model_dense, model_simple, model_lstm, model_gru, model_two_grus]
model_names = ["Dense", "SimpleRNN", "LSTM", "GRU", "2 GRUs"]
mse_val = {
    name: model.evaluate(X_val, y_val, verbose=0)
    for name, model in zip(model_names, models)
}
val_results = pd.DataFrame({"Model": mse_val.keys(), "MSE": mse_val.values()})
val_results.sort_values("MSE", ascending=False)

	Model	MSE
1	SimpleRNN	1.491682
0	Dense	1.429465
4	2 GRUs	1.358651
3	GRU	1.344503
2	LSTM	1.331125

The network with an LSTM layer is the best.

model_lstm.evaluate(X_test, y_test, verbose=0)

1.932026982307434

Test set

Y_pred = model_lstm.predict(X_test, verbose=0)
plt.scatter(y_test, Y_pred)
add_diagonal_line()
plt.xlabel("Actual")
plt.ylabel("Predicted")
plt.show()

plt.plot(y_test[:, 4], label="Melbourne")
plt.plot(Y_pred[:, 4], label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

plt.plot(y_test[:, 0], label="Brisbane")
plt.plot(Y_pred[:, 0], label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False)
plt.show()

plt.plot(y_test[:, 6], label="Sydney")
plt.plot(Y_pred[:, 6], label="GRU")
plt.xlabel("Time")
plt.ylabel("Change in HPI (%)")
plt.legend(frameon=False);

Package Versions

from watermark import watermark
print(watermark(python=True, packages="keras,matplotlib,numpy,pandas,seaborn,scipy,torch,tensorflow,tf_keras"))

Python implementation: CPython
Python version       : 3.11.12
IPython version      : 9.3.0

keras     : 3.8.0
matplotlib: 3.10.0
numpy     : 1.26.4
pandas    : 2.2.2
seaborn   : 0.13.2
scipy     : 1.13.1
torch     : 2.6.0
tensorflow: 2.18.0
tf_keras  : 2.18.0

Glossary

• autoregressive forecasting
• forecasting
• GRU
• LSTM
• one-step/multi-step ahead forecasting
• persistence forecast
• recurrent neural networks
• SimpleRNN