python,time-series,statsmodels,autoregressive-models

There is nothing wrong. That's the behavior of a stationary ARMA process where predictions converge to the mean. If you have fixed seasonality, then you could difference the time series at the seasonal lag, i.e. use a SARIMA, and the prediction would converge to a fixed seasonal structure. If you...

r,warnings,optional-parameters,autoregressive-models,mle

Then just use: model<-arima(file[,1],order=c(1,0,0)) predict(model,n.ahead=5) ...

matlab,neural-network,autoregressive-models

Right Shift Your graph shows a timeshift of 1 (not 2!) timestep(s). This is not ideal, but can happen when the delays are badly chosen which leads to this kind of delay pattern. (For further explanation have a look at this question on MATLAB CENTRAL. In fact, Greg Heath posted...

python,statsmodels,autoregressive-models

It works after adding a bit of noise, for example signal = np.ones(20) + 1e-6 * np.random.randn(20) My guess is that the constant is not added properly because of perfect collinearity with the signal. You should open an issue to handle this corner case better. https://github.com/statsmodels/statsmodels/issues My guess is also...