I used two different methods to forecast a time series data.
- The first one used is HoltWinters with Beta and Gamma as FALSE, since I don't see any trend or seasonality in the plot.
Below is the result from Box.test
Box.test(fore.holt.stat$residuals, type="Ljung-Box", lag=10) Box-Ljung test data: fore.holt.stat$residuals X-squared = 10.691, df = 10, p-value = 0.3821
The p-value is 0.3821
I used auto.arima on the data and below is the result
Box.test(fore.arima$residuals, type="Ljung-Box", lag=10) Box-Ljung test data: fore.arima$residuals X-squared = 14.724, df = 10, p-value = 0.1425
The p-value is 0.14
Question 1 :
Can I say that the first model is better since I have a higher p-vale?
Below are few other observations :
accuracy(fore.holt.stat) ME RMSE MAE MPE MAPE MASE ACF1 Training set 424.9864 10275.55 7930.602 0.8782302 9.251837 0.766108 0.02142331
accuracy(fore.arima) ME RMSE MAE MPE MAPE MASE ACF1 Training set 284.5242 7243.413 5371.42 -0.1874984 6.036736 0.5183941 0.0100049 Question 2 :
Which of the model is correct based on the
accuracy function output?
In both the models, the p-value is high, but the mean of errors is not close to zero.