From your comment, I understand that you are trying to solve the binary classification problem using your aggregated data and you are getting very poor results when you simply use the mean.

Depending on specifics of your data and the shape of your time series, there are several alternatives that you could try. Note, that you might need (significantly) more than just a single number per time series to solve your problem.

- In addition to the mean, you could use the quantiles or some other summary statistic, like standard deviation, min, or max.
- You could try to sample the data, i.e. instead of taking the entire time series, pick only the values that are minutes, hours or days a part. Or pick only mid-day values. The frequency of the sampling depends on your data.
- Or just pre-aggregate by calculating averages for every hour, day, month, etc.
- Additionally, you could calculate the periodicity of your time series and use it as a new feature.
- Or calculate some trends.
- Try to fit some standard time series models to your data, e.g. ARIMA and use the coefficients as informative features.
- Last but not least, use the domain knowledge re what could be relevant feature for your classification problem: the biggest jump (max first order difference), change of regime, etc.

**Edit**
I’d pick at least 10-20 features per time series generated as described above and apply logistic regression with LASSO or even xgboost.

After selecting 10-20 features per time series you also could try PCA to reduce the dimension.

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I need to summarize each of he csv because I have huge number of such csv's. I am using mean to do such summarization hence, I wanted to know if there are other plausible ways to summarize time series data. Coz I believe mean is being too flat. – Anurag Upadhyaya – 2017-12-09T10:42:13.733

Your question isn’t answerable as it stands now. 1) It is still unclear what you mean by “better”. Do you have any quality criterion / procedure that could tell you that mean is worse than let say standard deviation, or just first measurement of your time series? You can’t optimise your “compression approach” without the optimisation criterion. 2) If you can’t formulate the criterion yourself, try to explain us how you intend to use the aggregated data (mean). Do you pass it to some ML algorithm? What kind of ML? – aivanov – 2017-12-09T11:05:21.740

Okay so yes , I am aggregating each file by mean and then I am training a binary classifier on it. So the data is pretty imbalanced and mean is not giving me variables which are separable. Both classes have similar distribution among all the variables. So as the mean was used to aggregate I was thinking of using some other way of aggregation as the data is time series may be mean is not the right way. – Anurag Upadhyaya – 2017-12-09T11:55:19.773