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I have a timeseries classification dataset that has about 2000 data points (binary classification).

When I use a LSTM model straighly on data, I got really bad results (nearly 0.1). But when I use first order differences in timeseries I got more improve results (nearly 0.3).

Example of first order difference:
[1, 6, 6, 8, 9] = [5, 0, 2, 1]

Therefore, I concluded that LSTM works better when we provide more processed data.

While doing some Google search I discovered that there is something called second-order differences. However, I am not clear what is the objective of second order difference and how to calculate it. Please let me know your thoughts on this.

I am happy to provide more details if needed.

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    $\begingroup$ LSTM can work better not just "when we provide more processed data", but specifically because when you take first-order differences you make your data much more stationary. Without more details given, stationary timeseries has a better chance of being modelled properly. You don't need to do the second-order differenceing (differencing of the first differenced) right away, because it is only needed when your timeseries still fails to be stationary. That is related to statistical point of view. $\endgroup$ Oct 31, 2019 at 15:34
  • $\begingroup$ @AlexeyBurnakov Thanks a lot for your comment. However, still with first-order difference I get 0.3. My intention is to improve the results at least up to 0.6, In that case, what are the other things that you would recommend me to do. Looking forward to hearing from you :) $\endgroup$
    – EmJ
    Oct 31, 2019 at 15:38

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