Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting

A key reason Forecasting - predicting future values of time series, is a key for recent success of deep learning for forecasting is multitask component in many industries (Fildes et al. 2008). Applications univariate forecasting - sharing deep learning model parameters include forecasting supply chain and airline demand across all series, possibly with some series-specific (Fildes et al. 2008; Seeger, Salinas, and Flunkert 2016), financial scaling factors or parametric model components (Salinas, prices (Kim 2003), and energy, traffic or weather Flunkert, and Gasthaus 2019; Smyl 2020; Bandara, Bergmeir, patterns (Chatfield 2000). Forecasts are often required for and Hewamalage 2020; Li et al. 2019; Wen et al. 2017; Rangapuram large numbers of related time series, i.e., multivariate time series et al. 2018; Chen et al. 2018). E.g., the winner of forecasting, as opposed to univariate (single time series) the M4 forecasting competition (Makridakis, Spiliotis, and forecasting. For example, retailers may require sales/demand Assimakopoulos 2020) was a hybrid ES-RNN model (Smyl forecasts for millions of different products at thousands of 2020), in which a single shared univariate RNN model is used different locations - amounting to billions of sales time series.