TensorFlow (Python) implementation of DeepTCN model for multivariate time series forecasting.

Overview

DeepTCN TensorFlow

TensorFlow (Python) implementation of multivariate time series forecasting model introduced in Chen, Y., Kang, Y., Chen, Y., & Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network. Neurocomputing, 399, 491-501. https://doi.org/10.1016/j.neucom.2020.03.011.

Dependencies

pandas==1.3.4
numpy==1.19.5
tensorflow==2.7.0
tensorflow_probability==0.14.1
plotly==5.3.1
kaleido==0.2.1

Usage

import numpy as np
from deep_tcn_tensorflow.model import DeepTCN

# Generate two time series
N = 1000
t = np.linspace(0, 1, N)
e = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0.25], [0.25, 1]], size=N)
a = 40 + 30 * t + 20 * np.cos(2 * np.pi * (10 * t - 0.5)) + e[:, 0]
b = 50 + 40 * t + 30 * np.sin(2 * np.pi * (20 * t - 0.5)) + e[:, 1]
y = np.hstack([a.reshape(- 1, 1), b.reshape(- 1, 1)])

# Fit the model
model = DeepTCN(
    y=y,
    x=None,
    forecast_period=100,
    lookback_period=200,
    quantiles=[0.01, 0.1, 0.5, 0.9, 0.99],
    filters=4,
    kernel_size=3,
    dilation_rates=[1, 2],
    loss='nonparametric'
)

model.fit(
    learning_rate=0.01,
    batch_size=64,
    epochs=200,
    verbose=1
)
# Plot the in-sample predictions
predictions = model.predict(index=900)
fig = model.plot_predictions()
fig.write_image('predictions.png', width=750, height=650)

predictions

# Plot the out of sample forecasts
forecasts = model.forecast()
fig = model.plot_forecasts()
fig.write_image('forecasts.png', width=750, height=650)

forecasts

Owner
Flavia Giammarino
Flavia Giammarino
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