# How to use historic data (granularity at day level) for ML modeling?

There is a scenario where I have to use historic data which is at the day level for the past 5 years.
Actually it is water flow data, what quantity of water was flown on that particular day. I have to use this feature along with a few other features like material, coating, etc,. for EDA and prediction. I tried averaging it out but not useful.

Data is like this flow1,flow2, and flow3 (including other features not shown here) for each day on that particular route id. This continues for 5 years for many routes.

I am not able to figure out how to consolidate this data so that I can feed it to the model. I am trying to predict the corrosion in the pipeline.

Thanks

• What exactly are you predicting and at what granularity? Jul 30, 2020 at 16:21
• I have updated the question. Jul 30, 2020 at 17:24
• Do you have labels for the variable you are trying to predict? That is, do you have historical measurements of corrosion? Jul 30, 2020 at 17:48
• Yes, I have other variables and historical measurements of corrosion, but this flow rate was stored as separate data. Not able to figure out how to use this data(in pic), whether I should take avg or sum or .. for each year and make a new feature in the dataset Jul 30, 2020 at 18:03

If you have a fixed timestep for your corrosion measure both sum and average would work just fine, as this would be two features scaled by $$\dfrac{1}{n}$$, where $$n$$ is just the number of timesteps taken by your water flow measurement system in one timestep of your corrosion data measurements.