I'm working on hierarchical time series forecasting(python) and when I'm trying to fit the model with the entire data that I have I could see that the forecasts are constant all the time for some features. I couldn't able to understand where exactly the problem is and what are the possible approaches to fix this issue. Any sort of help would be great.
Thanks in Advance!!
I have faced a similar issue recently with hierarchical time series forecasting with no seasonality, where one of the 90 forecasts had no trend and no change in level over time, an exception in my set of predictions.
I have implemented Statsmodels Exponential Smoothing and I have tuned the hyperparameters roughly following Jason Brownlee's super helpful guide on How to grid search exponential smoothing.
No matter the grid search, this particular forecast remained flat.
I've added some extra parameters to the grid search, for instance, to identify the optimum amount of data points to train each of the 90 models in my hierarchical forecast and I've calculated the Z-scores of the year-on-year percentage change of the predictions, to skip predictions that were less than 5% likely given a reference period, even when they have the lowest error scores (this was needed because 2020 is probably an outlier in my dataset - time will tell).
And again, it was flat.
Well, maybe I should indeed do as Rob Hyndman suggests in his article and add random noise to this prediction so that the users are happier with what they see, even though this would increase the error.