Guide to STRIPE: Shape and Time Diversity in Probabilistic Forecast

Time-series forecasting is a crucial machine learning problem in various fields including the stock market, climate, healthcare, business planning, space science, communication engineering and traffic flow. Time-series forecasting is the systematic analysis of historic (past) signal correlations to predict future outcomes. Time-series forecasting can be grouped roughly into two classifications based on the model outputs: probabilistic time-series forecasting and deterministic time-series forecasting.  Probabilistic time-series forecasting aims to develop a distribution of predictions. Since the future is stochastic in nature, it is hard to arrive at a single prediction. Generative models such as cVAE and GANs mostly do follow this probabilistic approach in developing most-likely divers