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To me time is just one dimension. What is described is just the difference between interpolation and extrapolation. In terms of forecasting state of the art ar
by riedel 3y ago
To me time is just one dimension. What is described is just the difference between interpolation and extrapolation.
In terms of forecasting state of the art are weather models like graphcast or panguweather. I guess arima won't be much of help in those high dimensional cases.
If you consider the univariate case the trick to outperform arima I guess is to detect the context from the time window before to make better contextual predictions: this is much like a regression on a hidden variable.
- mr_toad 3y ago> What is described is just the difference between interpolation and extrapolation. Extrapolation (predicting an unknown future), and interpolation (estimating unknown present/past) are not really that different.
- riedel 3y agoI would mostly agree, this why time series imputation or cleaning is often not that different from time series forecasting (or rather often 'nowcasting'). You would only want to be careful what kind of validation you chose to test the generalisability of the approach. If you take however the example of the weather as an extreme example of time series forecasts, downsizing eddies or forecasting them in a navierstokes surrogate, this can require some different approaches.