Deep Probabilistic Time Series Forecasting over Long Horizons

Recent advances in neural network architectures for time series have led to significant improvements on deterministic forecasting metrics like mean squared error. We show that for many common benchmark datasets with deterministic evaluation metrics, intrinsic stochasticity is so significant that simply predicting summary statistics of the inputs outperforms many state-of-the-art methods, despite these simple forecasters capturing essentially no information from the noisy signals in the dataset. We demonstrate that using a probabilistic framework and moving away from deterministic evaluation acts as a simple fix for this apparent misalignment between good performance and poor understanding. With simple and scalable approaches for uncertainty representation we can adapt state-of-the-art architectures for point prediction to be excellent probabilistic forecasters, outperforming complex probabilistic methods constructed from deep generative models (DGMs) on popular benchmarks. Finally, we demonstrate that our simple adaptations to point predictors yield reliable probabilistic forecasts on many problems of practical significance, namely large and highly stochastic datasets of climatological and economic data.