BST832 - M4 Competition - Residuals Diagnostics - IT Assignment Help

Assignment Task:

BST832: M4 Competition Assignment Help

The Task
The objective of this coursework is to propose and build a framework for batch forecasting of fast moving time series. Once a set of suitable forecasting models is identified (Part A), you are asked to propose a model selection strategy that will automatically choose the most appropriate forecasting model for each time series individually (Part B). The proposed strategy should then be applied to all time series. Finally, performance evaluation (Part C) and residuals diagnostics (Part D) should be carried out.
The Data
Using the library M4comp2018 of the R statistical software, consider the monthly time series of the M4-Competition. You can use the following code to extract the monthly time series:

M4_monthly <- Filter(function(l) l$period == "Monthly", M4)

You need to randomly select 50 time series.

 Note that each time series is split in an in-sample (M4_monthly [[ID]]$x) and an out-of-sample (M4[[ID]]$xx) set of observations. Other useful variables include the size of the in-sample (M4_monthly [[709]]$n), the size of the out-of-sample (equal to the required forecast horizon, M4_monthly [[709]]$h), and the category of the data (micro, macro, industry, demographic, finance or other, monthly [[709]]$type). For simplicity, the length of the out-of-sample set is always 18 months. You are expected to use only the in-sample set in order to generate statistical forecasts for the out-of-sample set (forecasting horizon equal to out of sample). Then, forecasting performance should be evaluated by comparing the produced forecasts with the withheld out-of-sample set of observations.

Part A: Select a suitable toolbox of forecasting models
Your toolbox should contain exponential smoothing, arima and regression models. 6 Up to a maximum of ten (10) models in total. The selected models should be able to capture collectively different underlying time series characteristics (level, trend, seasonality, autocorrelation). A full justification of the selected models should be provided. 

Part B: Select and apply a suitable model selection strategy
Using only the in-sample data, propose a suitable strategy in order to select for each series individually the most suitable forecasting model. Justify the selection of this model selection strategy over other model selection strategies for forecasting. Apply the proposed model selection strategy to the data in order to generate forecasts for the out-of-sample periods. 

Part C: Performance evaluation
Evaluate the forecasts produced in the previous step using at least three appropriate error measures. Evaluation should be carried out across time series and across horizons. Justify the selection of these error measures over other possible candidates. Compare the accuracy of the proposed selection strategy with that of three (3) suitable benchmarks (for example, Naïve or Damped Exponential Smoothing) when each of these is applied across all series. Was the application of the proposed model selection strategy successful for this set of data? Critically discuss.

Part D: Residuals diagnostics
Select the three time series in your set randomly and perform residuals diagnostics for the selected “optimal” and two more methods (one being exponential smoothing and the other a ARIMA model).