In my problem I have a longer period of historical data of a time series. I need to predict for some specific points in time in the future. For these points in time five previous values are also available.
So far my approach was to use a sliding window of size five, use lag features and apply machine learning methods.
However I have a feeling that when doing like this I am not exploiting the historical data to the full extent. (The methods see only one sliding window at a time.)
I am now looking for some method (or ideas to design my own) which takes as an input historical data and measurements just before the time point I need to predict for.
Thank you!
Best Answer
As you have the same information available in your past data as for your future data (both in the form of time series), you could use a number of sequence data/time series based model types to predict your target variable. The core difference is that those models "remember" information between samples, so don't only derive the output on from the $N$ input features of the current input, but also from input they've seen before. Such models range from e.g. Hidden Markov Models to Recurrent Neural Networks - but there are many more suitable model types, with different advantages/disadvantages depending on the details of your problem.
PS: have a look at e.g. this answer or this slideset for some more details and further references.