forecasting

Unable to find forecast.Arima function in forecast package. Error displayed "forecast.Arima" not found. Can forecast function be used in place of 'forecast.Arima' function ? I am using forecast 8.1. Secondly, the output from ARIMA is flat at mean for the future dates. Is this because I am using 'forecast' function. library(forecast) arima.forecast <- forecast(arima1, h=30) forecast.Arima is not missing, it is just not exported in v8.1+. Use forecast instead, which will call forecast.Arima when required. Flat forecasts are common. See https://robjhyndman.com/hyndsight/flat-forecasts/ 来源： https:

I'm using statsmodels to fit a ARMA model. import statsmodels.api as sm arma = sm.tsa.ARMA(data, order =(4,4)); results = arma.fit( full_output=False, disp=0); Where data is a one-dimensional array. I know to get in-sample predictions: pred = results.predict(); Now, given a second data set data2 , how can I use the previously calibrated model to generate a series with forecasts (predictions) based in this observations? I thought there was an issue for this. If you file one on github, I'll be more likely to remember to add something like this. The prediction machinery is not (yet) available as

I am trying to predict weekly sales using ARMA ARIMA models. I could not find a function for tuning the order(p,d,q) in statsmodels . Currently R has a function forecast::auto.arima() which will tune the (p,d,q) parameters. How do I go about choosing the right order for my model? Are there any libraries available in python for this purpose? behzad.nouri You can implement a number of approaches: ARIMAResults include aic and bic . By their definition, (see here and here ), these criteria penalize for the number of parameters in the model. So you may use these numbers to compare the models. Also