Feature importance of regressors, and forecast length longer than length of data #151
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Hi, Firstly, thanks a lot for creating such a great package! It has definitely added significant value in my ts forecasting methods and I appreciate you putting this together. I typically use the future_regressor function with multiple regressors given there are a number of variables that are known based on other forecasts. Does the AutoTS function provide details of which regressors are chosen and how they are weighted? Secondly, do the models account for leads/lags of these future regressors? i.e. if I am forecasting GDP, but I have data on interest rate forecasts, which might typically impact GDP 6 months after interest rates increase/fall. Do the models account for this and find the optimal lag of future regressors? Can this data also then be used to create a longer forecast? i.e. if current interest rate rises will impact the GDP in 6 months time, but I also have a forecast of the next 6 months for interest rate rises, which would impact GDP in 12 months time. This should theoretically allow me to forecast 12 months ahead, as opposed to the 6 months of data I have available for future_regressor. I understand the complexities of this, given different models will have different lags and impacts of future regressors, but I was wondering if this was possible? |
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Replies: 2 comments 8 replies
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@k-dhingra I'm glad you like it! AutoTS does not itself provide details of future regressor usage other than the Does it find the optimal lag for future_regressors? No, it does not. That is something which is on my wishlist to figure out, but the only way I know right now is to loop through all possible lags. That is extremely computationally intensive, and often times finds the wrong lag anyway due to spurious correlations and the like. You can of course manually lag the data forward if you already know what the optimal lag is - the datetime index of the future regressor is for when you want the data applied, it doesn't have to be for when the data came from. For your case, it sounds like manually shifting the data forward would work just fine, giving you the 12 months. You could also pass a future_regressor that contains the same data at different lags - should not hurt, and might help. |
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*additional comment deleted as spam. First time I've seen that on GitHub |
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@k-dhingra I'm glad you like it!
AutoTS does not itself provide details of future regressor usage other than the
regressor_used
param, which is whether all were used, or not (and of course, some models never use regressors). The one exception is the Cassandra model, which has regressor coefficients available for each (let me know if you need help with using that, as it isn't well-documented). Some Statsmodels package models would also provide this information, but that is not accessible directly - you'd have to rerun the model in statsmodels directly to get at it.Does it find the optimal lag for future_regressors? No, it does not. That is something which is on my wishlist to figure out, …