Menu
The usual way to fit an ARIMA model with the statsmodels python package is:
model = statsmodels.tsa.ARMA(series, order=(2,2))
result = model.fit(trend='nc', disp=1)
however, i have multiple time series data to train with, say, from the same underlying process, how could i do that?
749
This model is the basic interface for ARIMA-type models, including those with exogenous regressors and those with seasonal components. The most general form of the model is SARIMAX(p, d, q)x(P, D, Q, s). It also allows all specialized cases, including. autoregressive models: AR(p)
1- Check again the stationarity of the time series using augmented Dickey-Fuller (ADF) test. 2- Try to increase the number of predictors ( independent variables). 3- Try to increase the sample size (in case of monthly data, to use at least 4 years data.
ARIMA is a method for forecasting or predicting future outcomes based on a historical time series. It is based on the statistical concept of serial correlation, where past data points influence future data points.
Non-seasonal ARIMA models are generally denoted ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is the order (number of time lags) of the autoregressive model, d is the degree of differencing (the number of times the data have had past values subtracted), and q is the order of the moving-average ...
When you say, multiple time series data, it is not clear if they are of the same type. There is no straightforward way to specify multiple series in ARMA model. However you could use the 'exog' optional variable to indicate the second series.
Please refer for the actual definition of ARMA model.
model = statsmodels.tsa.ARMA(endog = series1, exog=series2, order=(2,2))
Please refer for the explanation of the endog, exog variables.
Please see a working example of how this could be implemented
77
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
