Contents
What is hierarchical forecasting?
Hierarchical time series forecasting is the process of generating coherent forecasts (or reconciling incoherent forecasts), allowing individual time series to be forecast individually, but preserving the relationships within the hierarchy.
Why is forecasting important?
Forecasting is valuable to businesses because it gives the ability to make informed business decisions and develop data-driven strategies. Past data is aggregated and analyzed to find patterns, used to predict future trends and changes. Forecasting allows your company to be proactive instead of reactive.
How is forecasting done?
Forecasting addresses a problem or set of data. The data is analyzed, and the forecast is determined. Finally, a verification period occurs where the forecast is compared to the actual results to establish a more accurate model for forecasting in the future.
What is the challenge of hierarchical time series forecasting?
The entire challenge of hierarchical time series forecasting (this name also includes grouped and mixed cases, just to be clear) is to generate forecasts that are coherent across the entire aggregation structure. By coherent, I mean forecasts that add up in a manner that is consistent with the underlying aggregation structure.
Effectively, the coherent forecasts are a weighted sum of all the base forecasts from all the levels. To find the weights, we need to solve a system of equations to ensure that the hierarchical relationship between the different levels is preserved. unbiased forecasts at all levels with minimal loss of information
Are there any articles on time series forecasting?
Most of the articles on time series forecasting focus on a particular level of aggregation. However, the challenge appears when we can drill down our aggregated data to observe the same series on a more granular level.
What are the different types of forecasting methods?
1 Straight-line Method. The straight line method is one of the simplest 2 Moving Average. Moving averages is a smoothing technique that looks at the underlying pattern 3 Simple Linear Regression. Regression analysis is a widely used tool for analyzing 4 Multiple Linear Regression. A company uses multiple linear regression