I have a timeseries with a sample every 5 minutes. I want to forecast the timeseries multiple step ahead (e.g., 60 minutes, which is 12 samples ahead) using its past values. Unfortunately my model does not seem to capture well the dynamics of the timeseries. Maybe it comes from my lack of data.
My question is then the following: can upsampling (e.g., going from one sample every 5 minutes to one sample every minute, and then filling the gaps through interpolation) can be an effective means to augment my dataset and thus help my model capturing the dynamics of the timeseries?
What do you think? I did not find any paper covering the idea. I have seen the following cross-validated post covering data augmentation in timeserie: Data Augmentation strategies for Time Series Forecasting. While quite interesting, it is not dealing with the simple idea I am asking about.