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I really haven’t paid attention to my own water consumption; out of all the utilities this is the cheapest bill for me.  But for fun I thought I would try to forecast my consumption and compare the predictive capabilities of both Lumira and Predictive Analytics.


Above is my data acquisition in Excel; consumption is recorded on a quarterly basis.


I create a date/time hierarchy on the date


Above shows the Predictive options


I’m not sure I agree with the forecast, but it was interesting to see my consumption over the years.  Around 2004-2005 time frame, I had a water garden in my back yard; I didn’t want to keep up with it so I stopped the water from feeding the garden.  Then a few years later I disconnected my dishwasher when it stopped working and I decided not to replace it.  Hence, my water consumption is minimal.

Unlike California, where I live we’ve experienced plenty of rain so there’s been no droughts.  My decrease in consumption more had to do with stopping the water garden and stop using the dishwasher.   I never tied the two events to a decrease in water consumption.

For fun, I thought I would try this in SAP Predictive Analytics.  I didn’t have to do any data preparation, I just loaded the spreadsheet and selected an algorithm:


I tried triple exponential smoothing


Above are the parameters


Above are the results from triple exponential smoothing

I changed the algorithm to R Triple Exponential Smoothing and the results look better to me:


I don’t understand the spike in 2018 but the predicted values look more reasonable.

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  1. Paul Pallath


    Typically  the confidence on the near term forecast is higher, as compared to a forecast that is generated for a time frame much in the future.

    Now normally one would create the time series model and use it for forecasting on a windowing manner. So as the actual window nears the 2017 timeframe, the forecast will be more accurate for 2018.
    I hope this helps…



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