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will i need to consider 1 and 11 in ACF graph  as a q non seasonal values in SARIMA. if d=1 and D=1 and there is seasonal lag at 12,24 and 36. 

Based on the Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots you provided for the dataset from Jamnagar, we can discuss how to handle non-seasonal and seasonal parameters in SARIMA modeling (Seasonal Auto-Regressive Integrated Moving Average). 

### Interpretation of ACF and PACF:

1. **ACF Plot**:
   - The ACF graph shows significant spikes at lags 1 and 11, which are outside the confidence limits. This suggests that a Moving Average (MA) component might be needed to account for these dependencies.
   - Seasonal lags at 12, 24, and 36 are present as you mentioned, indicating strong seasonal correlation.

2. **PACF Plot**:
   - The PACF plot also shows significant spikes, especially at lag 1 and 11, which typically suggests the need for an Auto-Regressive (AR) component at those lags.

### SARIMA Configuration:

- **Non-seasonal part (ARIMA)**: You mentioned \( d = 1 \) (the number of differences needed to make the series stationary). This means that you are working with differenced data.
   - Considering the ACF has a spike at lag 1, it could indicate an MA(1) component.
   - The PACF's spike at lag 1 might suggest an AR(1) process.
   - The spike at lag 11 might suggest adding an MA(11) or AR(11) component, but these are less common.

- **Seasonal part**: You noted the presence of a seasonal pattern at lags 12, 24, and 36, and you set \( D = 1 \) (seasonal differencing). Given that there is autocorrelation at seasonal lags, consider the following for the seasonal part:
   - Seasonal AR and/or MA components at lag 12 (and possibly lag 24 or 36, if needed).

### Conclusion:
- **Non-seasonal part (p, d, q)**: You might need \( p = 1 \) and \( q = 1 \), based on the PACF and ACF at lag 1. However, lag 11 suggests some potential for an MA(11) component. It would be uncommon but not impossible.
  
- **Seasonal part (P, D, Q, S)**: 
   - \( P = 1 \) or \( Q = 1 \), depending on how you model the seasonal lags (likely at 12).
   - Seasonal period \( S = 12 \) (as indicated by seasonal lags at 12, 24, and 36).

Thus, a potential SARIMA model could be:  
\[ \text{SARIMA}(1,1,1) \times (1,1,1)_{12} \]

You might want to experiment with whether adding an MA(11) helps improve the model fit.

Would you like more detailed analysis or help with any specific aspects of the SARIMA model?

### Related Questions:
1. How do you test if lag 11 significantly improves the SARIMA model?
2. What are the criteria for choosing between AR and MA components in SARIMA models?
3. How do you assess the goodness of fit for SARIMA models?
4. Could adding an MA(11) affect seasonal components at lags 12, 24, or 36?
5. How does differencing impact the interpretation of ACF and PACF in seasonal data?

**Tip**: Always check residual diagnostics (like ACF of residuals) after fitting the SARIMA model to ensure no significant autocorrelations remain.

Will I need to consider 1 and 11 in ACF graph as a q non-seasonal values in SARIMA if d=1 and D=1 and there is seasonal lag at 12, 24, and 36?

Explore how to identify non-seasonal and seasonal components in a SARIMA model using ACF and PACF plots for a time series from Jamnagar. This analysis highlights spikes at lags 1 and 11, suggesting potential MA and AR components, as well as seasonal lags at 12, 24, and 36.

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