Question: Time Series Missing Data?
fdelcab opened this issue · comments
Hi everyone,
I would like to know your opinion about seqtopoint deep learning models for forecasting and missing data.
I am dealing with a time series that has a lot of time discontinuity data, so when I resample the data, e.g. to 1 minute, I end up with a bunch of missing gaps.
- Should I include them in the input data (e.g. for a LSTM model) so I don't loose the time continuity characteristic of my time series?
- Will the NaN values will help in the generalization of the model, since they can be related with noisy data?
- How does LSTM model handles missing data (NaN values)?
Thank you for your time
Missing values can be completed using a variety of strategies, such as 0 fill, linear fill, fill with adjacent values, etc. There may be more advanced methods. I don't know how to evaluate performance between these methods
Hey, this is a really great question. We believe that it's a bit outside the scope of the playbook, and we don't have any specific recommendations. So we're closing this issue for now.