I am testing LSTM models to predict numbers of infections over time. I am testing different input (“lookback”) and output (“pred_length”) lengths as well as delays between the last data point available and the data point first being forecast (“lead”). I am currently looking at data simulated under a multistrain epi model, which generates yearly fluctuations in cases of two viruses (eg flu H1 and H3). I am happy to edit to share code + data, but so as not to drown readers, I will share what I think is important.
I am carrying out rolling forecasts: at each forecasting timepoint (represented by lines on the graph), I re-train/test/forecast. Forecasts improve over time as the model sees more and more past data.
My question is: I would expect forecasts to do best for time points closest in time to the data available, so I would expect accuracy for the first time point in each forecast to be highest and then for accuracy to decrease for the following timepoints predicted, but I do not see that, why not? I think this is to do with the impact of the long memory in the LSTM, but I would love to know more. Or is this a coding/ model problem – Would changing the model help?