# What are the best ways to use a time series data for binary classification

I have large number of csv files and each of them are timeseries based csv files sampled at Avery 5 seconds for 2-3 mins. I have 20k such files with 200-300 variables in each file. I am aggregating the data by mean over the entire 2-3 mins window are using it for binary classification.

Currently I am using mean of each column in the .CSV file to represent that file, so basically I am summarizing the csv's using one scalar value per column. so each file is one sample represented by its respective mean value. Could anyone suggest me some better ways to summarize the timeseries data.

Thanks for your time.

• 1) Why do you have to summarise it by only one number? 2) what do you mean by „better“? What is your criterion? Commented Dec 8, 2017 at 22:10
• 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. Commented Dec 9, 2017 at 10:42
• 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? Commented Dec 9, 2017 at 11:05
• 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. Commented Dec 9, 2017 at 11:55

## 2 Answers

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.

1. In addition to the mean, you could use the quantiles or some other summary statistic, like standard deviation, min, or max.
2. 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.
3. Or just pre-aggregate by calculating averages for every hour, day, month, etc.
4. Additionally, you could calculate the periodicity of your time series and use it as a new feature.
5. Or calculate some trends.
6. Try to fit some standard time series models to your data, e.g. ARIMA and use the coefficients as informative features.
7. 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.

• Thanks for your ideas, I have used std, variance, skewness and kurtosis as well as a single number representation.Do you have ideas on how to use this kind of data to perform binary classification. Because if I don't agreegate data as single number I won't be able to frame the problem for classification. Commented Dec 9, 2017 at 13:21
• I don’t understand why you are limited to single number only. Can you please explain? The classifier can take into account multiple features at the same time. Please provide some snippet of your data including the class label. Commented Dec 9, 2017 at 13:25
• The reason is each CSV has 100-200 columns and there are 20k csv's. So , I am currently aggregating the columns of each CSV as a single point value, so I get 20k samples of 100-200 features and then I train a classifier on it. Commented Dec 9, 2017 at 13:41
• What is your AUC? Commented Dec 9, 2017 at 14:01
• My auc is around .81 on test data. Commented Dec 9, 2017 at 14:20

You can try taking Fourier transform of the series if your domain suggests that frequency elements might have some meaning or relevance for classification. I once took top 10 coefficients of the transform along usual statistical features as suggested by aivanov. It helped me classify my data. Before taking the transform, you might also benefit from passing the series through a high-pass/low-pass or a band filter.