Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

Abstract

Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence between interacting time series. However, modeling statistical dependencies can improve accuracy and enable analysis of interaction effects. Deep learning methods are well suited for this problem, but multivariate models often assume a simple parametric distribution and do not scale to high dimensions. In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow. This combination retains the power of autoregressive models, such as good performance in extrapolation into the future, with the flexibility of flows as a general purpose high-dimensional distribution model, while remaining computationally tractable. We show that it improves over the state-of-the-art for standard metrics on many real-world data sets with several thousand interacting time-series.

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• Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
• A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
• Adjusting for Autocorrelated Errors in Neural Networks for Time Series
• Flow-based Spatio-Temporal Structured Prediction of Motion Dynamics
• Demand Forecasting of Individual Probability Density Functions with Machine Learning
• Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
• Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System