TIME SERIES PROJECT

Time series analysis is a vital tool for businesses to forecast future requirements whether it is sales, human resources or expenses. If we are interested in forecasting sales for company XYZ for next year, it will be necessary for us to collect or observe sales data regarding the company for several past years. This data will then be used to develop a model that may be useful in predicting the future sales.

You will therefore be required to collect historical unadjusted (i.e. should not be adjusted for seasonality) data for eight years, either monthly or quarterly observations that will be used for your time series project. The project is a requirement for the successful completion of this course. The format for completing the project is identified below.

• Consult the course calendar for the project due date.

Some Data Sources

• This particular site can be found under Algonquin College; learning resource centre; databases; E-STAT: http://www . statcan.ca

• Click on Learning Resources (left hand side of page)

• Click on Estat (right hand side on page)

• Click on "Accept and Enter". Userid : calgoed Password: estat

• There are several choices of data either adjusted or unadjusted for seasonality; quarterly, monthly or annually.

• There are many other sources included company's websites, United States Government sites and Fortune 500 company sites.

TIME SERIES PARTICULARS

You must perform a complete time series analysis, making use of the following guidelines:

Guidelines:

The following are guidelines and not complete. Be creative in your explanation!

1. Table of Contents
   - It should list, in chronological order, the important topics covered in the project along with the page numbers.
2. Introduction
   - Give a brief description of each of the four time series components. You should explain the time series model and the respective assumptions. You should also explain the intent of the report and if possible, provide background information on your dependent variable.
3. Seasonal Variation
   - Calculate and interpret the four quarterly (or 12 monthly) seasonal indexes. Also graph and explain (as to the effect on your dependent variable) the seasonal indexes.
4. Trend Analysis
   1. Based on the time series (original data) graph, describe the time series (i.e. before de-seasonalizing).
   2. On the same graph as (a) graph the de-seasonalized time series.
   3. Write out the Trend line equation provided by your computer output and explain the meaning of the components (b 0 , b 1 ) as they apply to your dependent variable. (De-seasonalize your data before finding the Trend Line).
5. Cyclical-Irregular variation
   1. Describe what are the common features of a cycle and indicate whether you feel your data exhibits any of these. i.e. does it reflect the current economic conditions?
   2. Examine your data and comment on the presence of irregular variation. Can you tie it to an event?
6. Forecasting
   - Based on the trend equation you have obtained from your project, and the seasonal indices, predict the values for the last 4 quarters or 12 months.
   1. Show all detailed calculations.
   2. Plot the actual and predicted values against time on the same graph.
7. Discussion
   - Discuss the shortcomings and evaluate the accuracy of your predictions.
8. Conclusion
   - Draw a conclusion based on your analysis. Evaluate the usefulness of your model for short-term forecasting. Are there limitations? Are there other variables, other than time, that should be considered?
9. Bibliography
   - You must identify your references: periodicals, etc.
10. Appendices
    - Include the following:
    1. Copy of the data.
    2. Output of statistical values.
    3. Calculation of seasonal indices.
    4. Calculation of Trend Line
    5. Graph of original data and the trend line
    6. Graph of Seasonal indices
    7. Graph of Predicted and actual values vs. time

Price: $18.86

Solution: The downloadable solution consists of 11 pages, 786 words and 11 charts.
Deliverable: Word Document