Moving averages with MongoDB's aggregation framework?

Question

If you have 50 years of temperature weather data (daily) (for example) how would you calculate moving averages, using 3-month intervals, for that time period? Can you do tha

Answer 1

The way I would tend to do this in MongoDB is maintain a running sum of the past 90 days in the document for each day's value, e.g.

{"day": 1, "tempMax": 40, "tempMaxSum90": 2232}
{"day": 2, "tempMax": 38, "tempMaxSum90": 2230}
{"day": 3, "tempMax": 36, "tempMaxSum90": 2231}
{"day": 4, "tempMax": 37, "tempMaxSum90": 2233}

Whenever a new data point needs to be added to the collection, instead of reading and summing 90 values you can efficiently calculate the next sum with two simple queries, one addition and one subtraction like this (psuedo-code):

tempMaxSum90(day) = tempMaxSum90(day-1) + tempMax(day) - tempMax(day-90)

The 90-day moving average for at each day is then just the 90-day sum divided by 90.

If you wanted to also offer moving averages over different time-scales, (e.g. 1 week, 30 day, 90 day, 1 year) you could simply maintain an array of sums with each document instead of a single sum, one sum for each time-scale required.

This approach costs additional storage space and additional processing to insert new data, however is appropriate in most time-series charting scenarios where new data is collected relatively slowly and fast retrieval is desirable.