Hitting Time Forecasting: The Other Way for Time Series Probabilistic Forecasting
How long does it take to reach a specific value?
The ability to make accurate predictions is fundamental for every time series forecasting application. Following this purpose, data scientists are used to choosing the best models that minimize errors from a point forecast perspective. That’s correct but may not be always the best effective approach.
Data scientists should also consider the possibility of developing probabilistic forecasting models. These models produce, together with point estimates, also upper and lower reliability bands in which future observations are likely to fall in. Despite probabilistic forecasting seeming to be a prerogative of statistical or deep learning solutions, any model can be used to produce probabilistic forecasts. The concept is explained in one of my previous posts where I introduced conformal prediction as a way to estimate prediction intervals with any scikit-learn models.
For sure a point forecast is considerably easier to communicate to non-technical stakeholders. At the same time, the possibility to generate KPIs on the reliability of our predictions is an added value. A probabilistic output may carry more information to support decision-making. Communicating that there is a 60%…
