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I am relatively new to time series. My goal is to predict a few hours of data, measured every 15min based on three months of observations in Python. I assume I have daily and weekly cycles which I want to estimate and apply ARMA on residuals obtained from subtracting this seasonal trend. I am using statsmodels-0.8.0, the latest version from master branch.

I tried several approaches which did not work. Any input is much appreciated, if you can comment on possible errors or suggest further steps, or better approaches. The list of things I tried:

  1. SARIMAX on the original series with seasonal period 96(number of 15min periods per day) kills my Jupyter notebook kernel.

  2. statsmodels.tsa.seasonal.seasonal_decompose complains about 15 minute frequency, specifically "freq T not understood".

  3. residuals from subtracting trends obtained by running bkfilter, hpfilter, and cffilter are not stationary.

  4. I tried to model $y_t = \alpha * y_{t-96} + \beta * y_{t-96*7} + ARMA(p,q)$ by passing lagged values to ARIMA as exogenous variables, but got a warning that Likelihood optimization failed to converge, and for some p, q values, coefficients of AR or MA coefficients turned out to be nan's.

  5. periodogram() from statsmodels.tsa.stattools gave me the highest frequencies at periods 62 and 9, but I do not know how to compute fourier coefficients and estimate the trend. I am also concerned that spectral analysis approach is overfitting my data - do I get the same periods if I take a different three month of readings?

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  • $\begingroup$ I am also having trouble with issue 4, except I got that with SARIMAX itself. I think the model can't be fitted if the order is too high, except that the correlogram is indicating such high orders. $\endgroup$ – Milind R Nov 13 '17 at 6:06

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