Deep Learning-based DeepAR for Time Series Probabilistic Forecasting
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Real-world applications bring forth innovations for time series forecasting. Here let’s consider two use cases. An electricity company needs to forecast the energy consumptions of millions of individual households in a geographical area. The predictions can enable the electricity company to plan and allocate resources at household level. The second use case comes from e-commerce businesses (like Amazon.com). They need to forecast the inventories of individual products. They even need the forecasts for new products that have no historical data. In both cases, prediction intervals are needed to quantify uncertainty.
These use cases demand modeling techniques that can forecast multiple periods. The techniques should model multiple related time series all together, i.e., the global models. The techniques should provide prediction intervals. In short, the techniques should provide multi-period, multi-series forecasts with uncertainty.
