  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 later 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 demonstrate the effectiveness of the components comprising GenF.
