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.
Read moreHow do you predict in Python?
Python predict() function enables us to predict the labels of the data values on the basis of the trained model. The predict() function accepts only a single argument which is usually the data to be tested.
Read moreIs ARIMA a predictive model?
ARIMA, short for ‘AutoRegressive Integrated Moving Average’, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.
Read moreHow are parameters calculated in ARIMA?
Rules for identifying ARIMA models. General seasonal models: ARIMA (0,1,1)x(0,1,1) etc. Identifying the order of differencing and the constant: Rule 1: If the series has positive autocorrelations out to a high number of lags (say, 10 or more), then it probably needs a higher order of differencing .
Read moreWhat is P and Q in ARIMA?
A nonseasonal ARIMA model is classified as an “ARIMA(p,d,q)” model, where: p is the number of autoregressive terms, d is the number of nonseasonal differences needed for stationarity, and . q is the number of lagged forecast errors in the prediction equation .
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