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Chapter 7 Assignment: Time Series Overview

Part 1: Collecting the Data

Chapter 7 Assignment: Time Series Overview This assignment asks you to use forecasting tools to predict the price of gas based on the historical record of prices in Ottawa. Note: The forecasting can be done either directly in Excel or by utilizing the built-in functionality of the Solver plugin Part 1: Collecting the Data Download the SourceData.csv file from Moodle and save it as an excel file. Manipulate the data to isolate only the data that we are interested in — the price of Regular Unleaded Gasoline in Ottawa, on or after June 6, 2005. Place this data (copy/paste) in a separate worksheet, name this worksheet OttawaRegGas. This new sheet is what you will use for your forecast. Part 2: Creating a Forecast Create a moving average forecast to predict gas prices for the week following the final price from the source data. Use both 4 and 10 day moving average windows.
Part 3: Adding Trends to the Model 3A: Exponential Smoothing Use exponential smoothing to develop a forecast for the next 3 weeks. Use optimization for the constant. 3B: Adding a Trend (Holt's Method) Develop an appropriate forecast using exponential smoothing with a trend (Holt's method). Predict 3 weeks. Use optimization for the constants. 3C: Adding Cyclicality (Holt-Winter's Method) Develop an appropriate forecast using exponential smoothing with a trend and cyclicality (Holt-Winter's - multiplicative method). Predict 3 weeks. Use optimization for the constants. Use 26 for the period. Do not utilize "update forecast each time". Part 4: Choosing the Best Model Which model, of all the forecasts performed in this assignment, is the best?

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