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I'm trying to forecast timeseries with ARIMA. As you can see from the plot, the forecast is one step ahead of the expected values. I read in some other threads that this behavior is expected but how? How can I synchronize? enter image description here

The code I used:


history = [x for x in train]
predictions = list()

for t in range(len(test)):
    model = ARIMA(history,order=(2, 2, 1))
    model_fit = model.fit(disp=0)
    output = model_fit.forecast(alpha=0.05)
    yhat = output[0]
    predictions.append(yhat)
    obs = test[t]
    history.append(obs)
    print('predicted=%f, expected=%f' % (yhat, obs))

rmse = sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)
# plot
plt.plot(test, color='blue')
plt.plot(predictions, color='red')
plt.show()

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  • $\begingroup$ Please share the links to threads being referred in the statement "read in some other threads that this behavior is expected". $\endgroup$ Oct 17, 2022 at 17:13

2 Answers 2

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It appears you are using Python's statsmodel ARIMA.

There are two options:

  1. Change order= agrument. The p, d, or q hyperparameters might be causing that result.
  2. Manually lag the predictions predictions = predictions[1:].
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enter image description here

Your predicted curve (the one colored in red) is FOLLOWING your input (blue) curve. At time t, if blue goes down, then at time t+1, red will go down. You can see this behavior happening twice very clearly in the image once around t = 10 and then second time around t = 20. Also, the magnitude of change in the predicted curve is not as big/low as the input curve.

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