README.MD

DeepTCN TensorFlow

TensorFlow implementation of multivariate time series forecasting model introduced in Chen, Y., Kang, Y., Chen, Y., and Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network. Neurocomputing, 399, 491-501.

Dependencies

pandas==1.5.2
numpy==1.23.5
tensorflow==2.11.0
tensorflow_probability==0.19.0
plotly==5.11.0
kaleido==0.2.1

Usage

import numpy as np

from deep_tcn_tensorflow.model import DeepTCN
from deep_tcn_tensorflow.plots import plot

# Generate some time series
N = 500
t = np.linspace(0, 1, N)
e = np.random.multivariate_normal(mean=np.zeros(3), cov=np.eye(3), size=N)
a = 10 + 10 * t + 10 * np.cos(2 * np.pi * (10 * t - 0.5)) + 1 * e[:, 0]
b = 20 + 20 * t + 20 * np.cos(2 * np.pi * (20 * t - 0.5)) + 2 * e[:, 1]
c = 30 + 30 * t + 30 * np.cos(2 * np.pi * (30 * t - 0.5)) + 3 * e[:, 2]
y = np.hstack([a.reshape(-1, 1), b.reshape(-1, 1), c.reshape(-1, 1)])

# Fit the model
model = DeepTCN(
    y=y,
    x=None,
    forecast_period=100,
    lookback_period=100,
    quantiles=[0.001, 0.1, 0.5, 0.9, 0.999],
    filters=3,
    kernel_size=3,
    dilation_rates=[1],
    loss='parametric'
)

model.fit(
    learning_rate=0.001,
    batch_size=16,
    epochs=300,
    verbose=1
)

# Generate the forecasts
df = model.forecast(y=y, x=None)

# Plot the forecasts
fig = plot(df=df, quantiles=[0.001, 0.1, 0.5, 0.9, 0.999])
fig.write_image('results.png', scale=4, height=900, width=700)