Forecasting has fascinated people for thousands of years, sometimes being considered a sign of divine inspiration, and sometimes being seen as a criminal activity. The Jewish prophet Isaiah wrote in about 700 BC
Tell us what the future holds, so we may know that you are gods.
One hundred years later, in ancient Babylon, forecasters would foretell the future based on the distribution of maggots in a rotten sheep’s liver. By 300 BC, people wanting forecasts would journey to Delphi in Greece to consult the Oracle, who would provide her predictions while intoxicated by ethylene vapours. Forecasters had a tougher time under the emperor Constantine, who issued a decree in AD357 forbidding anyone “to consult a soothsayer, a mathematician, or a forecaster
May curiosity to foretell the future be silenced forever.” A similar ban on forecasting occurred in England in 1736 when it became an offence to defraud by charging money for predictions. The punishment was three months’ imprisonment with hard labour!
Forecasting: Principles and Practice (2nd ed)
Rob J Hyndman and George Athanasopoulos: Some things are easier to forecast than others. The time of the sunrise tomorrow morning can be forecast precisely. On the other hand, tomorrow’s lotto numbers cannot be forecast with any accuracy.
The predictability of an event or a quantity depends on several factors including:
Some things are easier to forecast than others. The time of the sunrise tomorrow morning can be forecast precisely. On the other hand, tomorrow’s lotto numbers cannot be forecast with any accuracy.
Introduction to Forecasting in Machine Learning and Deep Learning
Forecasts are critical in many fields, including finance, manufacturing, and meteorology. At Uber, probabilistic time series forecasting is essential for marketplace optimization, accurate hardware capacity predictions, marketing spend allocations, and real-time system outage detection across millions of metrics.
In this talk, Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered.
Orbit for Forecasting
Orbit is a general interface for Bayesian time series modeling. The goal of Orbit development team is to create a tool that is easy to use, flexible, interitible, and high performing (fast computation). Under the hood, Orbit uses the probabilistic programming languages (PPL) including but not limited to Stan and Pyro for posterior approximation (i.e, MCMC sampling, SVI). Below is a quadrant chart to position a few time series related packages in our assessment in terms of flexibility and completeness. Orbit is the only tool that allows for easy model specification and analysis while not limiting itself to a small subset of models.