I am new to both ARIMA technique and R. Your suggesiton would be very much appreciated.

I am dealing with hourly data with strong seasonality and have used auto.arima to select a model:

Fit<-auto.arima(H_ts, seasonal=TRUE ,approximation=FALSE)

This is the model it returns: ARIMA(5,1,4)(1,0,0)[24]

Coefficients:

         ar1     ar2     ar3      ar4      ar5      ma1      ma2      ma3     ma4    sar1
      0.6361  0.4046  0.5212  -0.7154  -0.0334  -0.5638  -0.4464  -0.5704  0.6325  0.0917
s.e.  0.0977  0.1390  0.1109   0.0968   0.0181   0.0957   0.1282   0.1152  0.0821  0.0114

sigma^2 estimated as 14.24:  log likelihood=-12188.98
AIC=24399.96   AICc=24400.02   BIC=24470.34

However, the residuals ACF and PACF look quite suspicious:

Any idea what I can do to 'fix' this?

I am new to both ARIMA technique and R. Your suggestions would be very much appreciated.

I am dealing with hourly data with strong seasonality and have used auto.arima to select a model:

Fit<-auto.arima(H_ts, seasonal=TRUE ,approximation=FALSE)

This is the model it returns: ARIMA(5,1,4)(1,0,0)[24]

Coefficients:

         ar1     ar2     ar3      ar4      ar5      ma1      ma2      ma3     ma4    sar1
      0.6361  0.4046  0.5212  -0.7154  -0.0334  -0.5638  -0.4464  -0.5704  0.6325  0.0917
s.e.  0.0977  0.1390  0.1109   0.0968   0.0181   0.0957   0.1282   0.1152  0.0821  0.0114

sigma^2 estimated as 14.24:  log likelihood=-12188.98
AIC=24399.96   AICc=24400.02   BIC=24470.34

However, the residual ACF and PACF look quite suspicious:

Any idea what I can do to 'fix' this?

I am new to both ARIMA technique and R. Your suggesiton would be very much appreciated.

I am dealing with hourly data with strong seasonality and have used auto.arima to select a model:

Fit<-auto.arima(H_ts, seasonal=TRUE ,approximation=FALSE)

This is the model it returns: ARIMA(5,1,4)(1,0,0)[24]

Coefficients:

     ar1     ar2     ar3      ar4      ar5      ma1      ma2      ma3     ma4    sar1
  0.6361  0.4046  0.5212  -0.7154  -0.0334  -0.5638  -0.4464  -0.5704  0.6325  0.0917

s.e. 0.0977 0.1390 0.1109 0.0968 0.0181 0.0957 0.1282 0.1152 0.0821 0.0114

sigma^2 estimated as 14.24: log likelihood=-12188.98 AIC=24399.96 AICc=24400.02 BIC=24470.34

However, the residuals ACF and PACF look quite suspicious:

Any idea what I can do to 'fix' this?

I am new to both ARIMA technique and R. Your suggesiton would be very much appreciated.

I am dealing with hourly data with strong seasonality and have used auto.arima to select a model:

Fit<-auto.arima(H_ts, seasonal=TRUE ,approximation=FALSE)

This is the model it returns: ARIMA(5,1,4)(1,0,0)[24]

Coefficients:

         ar1     ar2     ar3      ar4      ar5      ma1      ma2      ma3     ma4    sar1
      0.6361  0.4046  0.5212  -0.7154  -0.0334  -0.5638  -0.4464  -0.5704  0.6325  0.0917
s.e.  0.0977  0.1390  0.1109   0.0968   0.0181   0.0957   0.1282   0.1152  0.0821  0.0114

sigma^2 estimated as 14.24:  log likelihood=-12188.98
AIC=24399.96   AICc=24400.02   BIC=24470.34

However, the residuals ACF and PACF look quite suspicious:

Any idea what I can do to 'fix' this?