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- Referencing Styles : Harvard
- Course Code: DATA4400
- Course Title: data-driven decision making and forecasting
- Words: 1200
- University: Kaplan Business School
- Country: AU
Given an example of a comprehensive forecasting application provide a description and interpretation of the techniques used.
A dataset from a retailer that has more than 45 stores in different regions (Public data from Kaggle) has been sourced. The data provided for the assessment represents one store. The objective of the assessment is to develop different demand forecast models for that store and compare the forecast models in terms of accuracy, trend, and seasonality alignment with the historical data provided. Students must calculate the Root Mean Square Error (RMSE) or Mean Absolute percentage Error (MAPE) on the test data to provide conclusions and construct a dashboard in a BI tool.
A shiny web app. has been built to forecast monthly data using different methodologies. The app allows you to divide the data set between train and test set, so you can evaluate the performance of the models. You can use the error metrics provided to evaluate the performance and accuracy of the model.
Report Structure And Content
- Analyse the results of the forecast methodologies used, in terms of:
- Alignment with the historical trend
- Alignment with the historical seasonality
- Use a BI tool to visualise the dataset and generate a forecast using the BI tool. Compare the forecast generated by the BI tool with the three forecasts generated by the Shiny app. Provide visualisations and describe your findings.
- Recommend improvements to the shiny app.