shesterg/probcast-tensorflow

TensorFlow implementation of ProbCast model for multivariate time series forecasting with generative adversarial networks.

ProbCast TensorFlow

TensorFlow implementation of multivariate time series forecasting model introduced in Koochali, A., Dengel, A., & Ahmed, S. (2021). If You Like It, GAN It — Probabilistic Multivariate Times Series Forecast with GAN. In Engineering Proceedings (Vol. 5, No. 1, p. 40). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/engproc2021005040.

Dependencies

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

Usage

import numpy as np
from probcast_tensorflow.model import ProbCast

# Generate two time series
N = 300
t = np.linspace(0, 1, N)
e = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0.25], [0.25, 1]], size=N)
a = 20 * np.cos(2 * np.pi * (10 * t - 0.5)) + e[:, 0]
b = 10 * 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 = ProbCast(
    y=y,
    forecast_length=30,
    sequence_length=60,
    quantiles=[0.1, 0.3, 0.5, 0.7, 0.9],
    generator_gru_units=[64, 32],
    discriminator_gru_units=[64, 32],
    generator_dense_units=16,
    discriminator_dense_units=16,
    noise_dimension=100,
    noise_dispersion=10,
)

model.fit(
    learning_rate=0.001,
    batch_size=32,
    epochs=100,
    verbose=True
)

# Plot the in-sample predictions
predictions = model.predict(index=270)
fig = model.plot_predictions()
fig.write_image('predictions.png', width=750, height=650)

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