Implementation of ForecastNet described in the paper entitled "ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting" by Joel Janek Dabrowski, YiFan Zhang, and Ashfaqur Rahman.
Link to the paper: https://arxiv.org/abs/2002.04155
ForecastNet is a deep feed-forward neural network multi-step-ahead forecasting of time-series data. The model is designed for (but is not limited to) seasonal time-series data. It comprises a set of outputs which are interleaved between a series of "cells" (a term borrowed from RNN literature). Each cell is a feed-forward neural network which can be chosen according to your needs. This code presents ForecastNet with two different cell architectures: one comprising densely connected layers, and one comprising a convolutional neural network (CNN).
The key benefits of ForecastNet are:
- It is a time-variant model, as opposed to a time-invariant model (In the paper we show that RNN and CNN models are time-invariant).
- It naturally increases in complexity with increasing forecast reach.
- It's interleaved outputs assist with convergence and mitigating vanishing-gradient problems.
- The "cell" architecture is highly flexible.
- It is shown to out-perform state of the art deep learning models and statistical models.
- A PyTorch implementation and a TensorFlow implementation are provided. The PyTorch implementation is recommended as it is is more complete and more generic.
- Both implementations provide a demonstration using a synthetic dataset. The PyTorch implementation will be easiest to adapt to your own dataset.
- The TensorFlow implementation is written for univariate time-series. The PyTorch implementation accepts multivariate datasets (it has been tested with univariate datasets and datasets with multivariate inputs and a univariate output).
- Please read the README files in each implementation's directory.