Business Forecasting
Content Introduction 1 Part 1. Examine the data, looking for seasonal effects, trends and cycles 2 Part2. Dummy Variables Model 3 Linear trend model 3 Quadratic trend model 5 Cubic trend model 7 Part 3. Decomposition and Box-Jenkins ARIMA approaches 8 First difference: 10 a. Create an ARIMA (4, 1, 0) model 10 b. Create an ARIMA (0, 1, 4) model 11 c. Create an ARIMA (4, 1, 4) 11 d. Model overfitting 12 Second difference 13 Forecast based on ARIMA (0, 1, 4) model 13 Return the seasonal factors for forecasting 14 Part 4. Discussion of different methods and the results 15 Comparison of different methods in terms of time series plot 15 Comparison of different models in terms of error 17 Assumptions and the discussion on the sensitivity of assumptions 18 Conclusion 18
Business Forecasting Coursework
Introduction
The data of this coursework were drawn from the UK national statistics. It is a quarterly series of total consumer credit gross lending in the UK from the second quarter 1993 to the second quarter 2009. In this coursework, the first 57data will be used to establish models and the latter 8 data will be used to test if the forecast is a good fit or not. Two forecasting methods will be used in this coursework, which are a regression with Dummy Variables method and a combination of the Decomposition and Box-Jenkins ARIMA approaches. In addition, further comparison will be made between models to select out the best fit one. Then the underlying assumptions of the chosen model and sensitivity of the model to these assumptions will be discussed. All the analyses are based on the outputs working out by SPSS software.
Part 1. Examine the data, looking for seasonal effects, trends and cycles
It is the fundamental process that to find out trend-cycle and seasonality, in order to create a certain model for further forecasting. Two approaches can be used to examine the data: analysing the time series plot or ACF plot.
Business Forecasting Coursework
Introduction
The data of this coursework were drawn from the UK national statistics. It is a quarterly series of total consumer credit gross lending in the UK from the second quarter 1993 to the second quarter 2009. In this coursework, the first 57data will be used to establish models and the latter 8 data will be used to test if the forecast is a good fit or not. Two forecasting methods will be used in this coursework, which are a regression with Dummy Variables method and a combination of the Decomposition and Box-Jenkins ARIMA approaches. In addition, further comparison will be made between models to select out the best fit one. Then the underlying assumptions of the chosen model and sensitivity of the model to these assumptions will be discussed. All the analyses are based on the outputs working out by SPSS software.
Part 1. Examine the data, looking for seasonal effects, trends and cycles
It is the fundamental process that to find out trend-cycle and seasonality, in order to create a certain model for further forecasting. Two approaches can be used to examine the data: analysing the time series plot or ACF plot.