I have a tabular raw data from sensors with associated label and i want to extract the time series features like mean,max,min and std from the data all the sensor data and form another table or export to csv file so that i can do classification task on that data.
Data table
For clarification: mean,max,min,std are not "time series features", they are data features in general.
Assuming that you want to do it in python, you should take a look at pandas.DataFrame class. Once you initialize a Dataframe object with your tabular data, you can call its methods DataFrame.min(), DataFrame.max(), DataFrame.mean(), DataFrame.std() for your purpose.
You can insert all these calculated characteristics into a new DataFrame and thereafter call Dataframe.to_csv() to export them in a csv file.
for-loop going through all samples in batches of 10s with 50% overlapping, then assign the batch in a DataFrame object (inside the loop) and then call the appropriate methods for the batch. It will work smoothly.
$\endgroup$
You can also use an open source python library called 'tsfresh' (https://tsfresh.readthedocs.io/en/latest/) to extract time series features
Perhaps you need to look at this self-contained blogpost on Machine Learning with Signal Processing Techniques on how to prepare your time series data and extract useful statistical estimate and feature for machine learning models. At the end an example is given for classification. I found it super useful and straightforward.
Somewhere in the middle of the post, this great method for the Detection of peaks in data is introduced as well.
I don't have enough reputation to leave a comment, but could you please provide some sample data so that we can help you better?
When you say mean, max, min, are you trying to aggregate multiple rows of data on a date column with these functions? Or, do you have a timespan/ datetime/ timestamp column that you want to use?
