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I have a dataset (timeseries from 2010 to 2019 rainfall data from various districts near vellore).

When I ran the ADF(Augmented Dickey-Fuller Test) i got my dataset to be Stationary! meaning no seasonality!

My question is that am I doing something wrong? because normally rainfall occurs more in particular months(rainy season ofc) So shouldn't there be seasonality in my dataset?

ADF Result

Results of Dickey-Fuller Test:

Test Statistic -1.770941e+01

p-value 3.507811e-30

#Lags Used 7.000000e+00

Number of Observations Used 3.644000e+03

Critical Value (1%) -3.432146e+00

Critical Value (5%) -2.862333e+00

Critical Value (10%) -2.567192e+00

According to this result my test statistic of -17.7 is very small compared to critical values -2.56(10%) Hence this means my data is already stationary!.

Dataset contains daily data so there are a lot of 0's too, does this affect the seasonality?

Thank you!

om purohit
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    How did you set the lag? Does it correspond to the expected seasonality of your data? – Guillaume Ansanay-Alex Apr 16 '21 at 19:02
  • rainfall from one day to the next will not have a unit root. Testing for unit root with daily lags will not detect annual seasonal trends. Seasonal pattern will also be stable over time. – Josef Apr 16 '21 at 20:16

1 Answers1

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Check the same with KPPS test with checking the seasonal Trend kpps(df,regression='ct')

The parameter regression = 'ct' will check over the seasonal trend

Devansh Desai
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