# How do you detect seasonality(multiplicative or additive) in a time series data?

Trying to apply seasonal_decompose on timeseries data. It looks something like this:

            modal_price
Period
2014-11-01  1469
2015-01-01  1258
2015-03-01  1112
2015-04-01  1373
2015-06-01  1370
2015-07-01  1406
2015-08-01  1520
2015-09-01  1860
2015-10-01  1436
2015-11-01  1455


Using freq=11 and seasonalcomponents are all Nan. What should i do in order to detect the seasonality type(multiplicative/additive) of this data?

• Is that sampled data? Typically, you cannot have missing data for time series analysis. But to your question, there are methods for determining seasonality, but perhaps it might help to understand what language and packages you want to use. Nov 1 '18 at 14:43
• Hey! There were missing vakues but I sampled it. I now have a consistent dataset. I use python and can use any package, just looking for a solution here. Nov 2 '18 at 15:04

Once divide the time-series by its Moving Average and once subtract the moving average from it.

If the seasonality is additive, then the result of subtract will have almost similar fluctuations in magnitude.

If it is multiplicative, then the division result has such a property.

Like the figures below from my course in Feature Engineering:

So the seasonality was multiplicative as the division has similar magnitudes of fluctuations.

Please note that more linear the dynamic of the time-series is, better this naive approach works.

Hope it helps!