Towards Better Long-range Time Series Forecasting using Generative Forecasting

• URL: http://arxiv.org/abs/2212.06142v1
• Date: Fri, 9 Dec 2022 13:35:39 GMT
• Title: Towards Better Long-range Time Series Forecasting using Generative Forecasting
• Authors: Shiyu Liu, Rohan Ghosh, Mehul Motani
• Abstract summary: We propose a new forecasting strategy called Generative Forecasting (GenF) GenF generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data. We find a 5% - 11% improvement in predictive performance (mean absolute error) while having a 15% - 50% reduction in parameters compared to the benchmarks.
• Score: 29.046659097553515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-range time series forecasting is usually based on one of two existing forecasting strategies: Direct Forecasting and Iterative Forecasting, where the former provides low bias, high variance forecasts and the latter leads to low variance, high bias forecasts. In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data. We theoretically prove that GenF is able to better balance the forecasting variance and bias, leading to a much smaller forecasting error. We implement GenF via three components: (i) a novel conditional Wasserstein Generative Adversarial Network (GAN) based generator for synthetic time series data generation, called CWGAN-TS. (ii) a transformer based predictor, which makes long-range predictions using both generated and observed data. (iii) an information theoretic clustering algorithm to improve the training of both the CWGAN-TS and the transformer based predictor. The experimental results on five public datasets demonstrate that GenF significantly outperforms a diverse range of state-of-the-art benchmarks and classical approaches. Specifically, we find a 5% - 11% improvement in predictive performance (mean absolute error) while having a 15% - 50% reduction in parameters compared to the benchmarks. Lastly, we conduct an ablation study to further explore and demonstrate the effectiveness of the components comprising GenF.

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