Time Series Forecasting:

The dataset provided captures electricity production from 1985 to 2017. You are tasked with building a forecasting model to forecast electricity production.

1. Given the data, perform a linear regression model, with the date as the order, and report the R-Squared and the RMSE. Plot your regression line against the actual values.
2. Given the data find the moving averages (MA) 5, 10, and 15 days and perform a pairwise correlation analysis between each MA and the parameter we are trying to forecast.
3. Given the data find the 10 previous lag values and perform a pairwise correlation analysis between each lag and the parameter we are trying to forecast.
4. Given the dataset and the parameters generated in the previous steps, Find the best combination of parameters that will give you the best RMSE and R-Squared.
5. Plot your best model's prediction against actual values.
6. Explain how you would know if your model is overfitting and how would you avoid it.

https://drive.google.com/file/d/16mvYE5KAgi__NukmiMkWu4dpAYY1wiRi/view?usp=sharing

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