I have a time series of sensor data from a machine. This machine is sometimes moved and thus there are big chunks of missing data, here is a plot of the data points:
My goal is to try to start building some basic forecasting models, I thought I would try Holt Winters, ARIMA, and Theta methods.
However I have noticed that the way these methods work (at least in R), is they expect a time series with constant time intervals instead of what I have:
> glimpse(test_df) Observations: 19,086 Variables: 2 $ System_Time <dttm> 2018-02-01 13:26:00, 2018-02-01 13:31:00,... $ System_Variable <int> 1240, 1400, 1210, 1270, 1230, 1170, 1180,...
I am fairly new to this and unsure how to proceed, as I can't use any functions like ts() without fixing this. My time series also says it has frequency = 1 even though it spans over several months.
I have tried running auto.arima just to see what happens and it just spits out a flat static line:
Trying to use holt winters is even worse, it just goes straight up.
I am unsure of what to do, I am guessing I could do something like interpolate? But that seems dodgy at best.