Hello Antonio,
If I understand correctly, you're referring to the challenge of handling time series data with varying lengths. There are several strategies to manage such datasets effectively.
One approach is to employ models like Recurrent Neural Networks (RNNs), which are designed to process sequences of data. For instance, consider a sequence like [a b c d e ...] of any given length. You could construct a new dataset for the RNN where 'n' consecutive values are used to predict the subsequent value. Let's say we choose 'n' to be 2; in this case, we would feed 'a' and 'b' into a standard RNN to forecast the value of 'c'. By using this method, you can reformat your data in a way that the inconsistency in the time series lengths won’t matter.
Plain Recurrent Neural Networks (RNNs) often encounter significant issues with vanishing and exploding gradients. To address these challenges, you might consider utilizing architectures such as Long Short-Term Memory (LSTM) networks. However, the method of preparing your dataset would remain consistent regardless of the network type you choose.
Time series having only one value carry no meaning as such and can be removed.
For training RNN and LSTM please go through this documentation respectively,
Also, to look at problems related to RNN please go through this link,
https://www.superdatascience.com/blogs/recurrent-neural-networks-rnn-the-vanishing-gradient-problem/
Hope this helps.
