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I am new to time series analysis, and I am wondering how I can approach forecasting having time series of different lengths. Specifically, each time series contains a sequence of ages and value. E.g.,

age_t value_t age_t-1 value_t-1

such as

12 210 11 205 10 203 9 203 ... 2 340 1 350
3 340 2 335 1 392

I want to forecast value_t+1. My problem is that I have time series of different lengths: for certain machines I have 15 years of history, for other machines I have 1-2 years of history.

Could anyone suggest a general way of approaching forecasting in this case, e.g., how to pre-process/transform the time series, or a method that is typically indicated in cases like this?

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  • $\begingroup$ You could assume a sequence length. If the length of the time series is greater than that of the sequence length, cut it. If it's smaller than that left pad it with (0, 0). $\endgroup$
    – Fariborz Ghavamian
    Commented Aug 30, 2019 at 13:57

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This should do the trick:

Independent variable (y): value_t

Dependent variables(features): age_t-1 value_t-1

Just add each row as examples jnto your ml model. If you predict value_t+1 or value_t is basically the same id the preprocessing is done correctly. Otherwise you could manually preprocess the features using pandas.

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