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I collect time series wind speed data. Occasionally, the anemometers that collect this data break. I have flag "events" in place to notify me when this occurs - they are very rudimentary, for example, when a sensor breaks, it will return flat line data. So I have code that checks for wind speed data < 1 m/s. Usually the sensors are not broken - flat line happened because temperature is below freezing or the wind is simply not blowing.

The problem: Many false positives.

Current solution is very rudimentary and rule based. Example pseudocode might be:

if windspeed < 1 AND temp < 0:
    return "Broken Sensor"

I have maybe 20 of these rules but I would like to develop a ML classifier that is more sophisticated. I thought about a decision tree where the features might be:

  • Wind Speed
  • Temperature
  • Barometric Pressure
  • Boolean: Is another sensor at the same height reading a similar value

And I would be seeking to predict whether the failure event is either False Flag or Real Event

However, this would model each time-stamp in the data as it's own separate row independent of each other, when in actuality my data is time series.

My questions:

  • Are there effective methods for modeling time series with decision trees?

  • Would you recommend another model altogether?

  • Should the data be modeling with booleans or actual values? For example, instead having temperature, I could just use boolean for freezing v. not freezing. Or does this matter?

I have wind direction and standard deviation data as well, maybe that could help predict as well.

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