In a study of potential effects of both climate and pollution on disease specific mortality between the years 2010-2020 a team of researchers studied the disease specific averaged weekly mortality in Paris, France and the city's local climate (temperature degrees Fahrenheit), size of pollutants and levels of noxious chemical emissions from cars and industry in the air - all measured at the same points between 2010-2020.

All 5 series i.e. mortality, temperature, pollutants particle size and two chemical emissions (chem1, chem2) between 2010-2020 (508 time points) are given here in mort .csv

You will use this data for the calculation of 4 weeks ahead forecasts for mortality.

Your task is to give best 4 weeks ahead forecasts in terms of R squared, AIC, BIC, MASE etc (as is appropriate) for the mortality series. Provide the point forecasts and confidence intervals and corresponding plot for the most optimal model for each method used (DLM, ARDL, polyck, koyck, dynamic, exponential smoothing and state-space model).

Multiple predictors are to be modelled i.e., use more than 1 predictor in regression type models, (i.e. multivariate). Point forecasts and confidence intervals should be reported along with appropriate graphs. Percentiles method for relevant covariates for the forecasts can be used.

Hint: Use MASE () function from the dLagM package to compute MASE for time series regression methods for model comparisons.