Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data
Effective long-range forecasting of time series data remains an unsolved and open problem. One possible approach is to use generative models to improve long-range forecasting, but the challenge then is how to generate high-quality synthetic data. In this paper, we propose a conditional Wasserstein GAN with Gradient and Error Penalty (cWGAN-GEP), aiming to generate accurate synthetic data that preserves the temporal dynamics between the conditioning input and generated data. By using such synthetic data, we develop a long-range forecasting method called Generative Forecasting (GenF). GenF consists of three key components: (i) a cWGAN-GEP based generator, to generate synthetic data for next few time steps. (ii) a predictor which makes long-range predictions based on generated and observed data. (iii) an information theoretic clustering (ITC) algorithm to better train the cWGAN-GEP based generator and the predictor. Our experimental results on three public datasets demonstrate that GenF significantly outperforms a diverse range of state-of-the-art benchmarks and classical approaches. In most cases, we find an improvement of at least 10% over all studied methods. Lastly, we conduct an ablation study to demonstrate the effectiveness of the cWGAN-GEP and the ITC algorithm.
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