1. INTRODUCTION TO FORECASTING
Chapter 1 – Getting Started

2. Nature and Uses of Forecasts

3. Forecasting problems occur in many fields:
Business and industry
Economics
Finance
Environmental sciences
Social sciences
Political sciences

4. Forecasting Problems Short-term forecasts Medium-term forecasts
Predicting only a few periods ahead (hours, days, weeks)
Typically bad on modeling and extrapolating patterns in the data
Medium-term forecasts
One to two years into the future, typically
Long-term forecasts
Several years into the future

5. Forecasting Problems Short-term forecasts Medium-term forecasts
are needed for the scheduling of personnel, production and transportation. As part of the scheduling process, forecasts of demand are often also required.
Medium-term forecasts
are needed to determine future resource requirements, in order to purchase raw materials, hire personnel, or buy machinery and equipment.
Long-term forecasts
are used in strategic planning. Such decisions must take account of market opportunities, environmental factors and internal resources.

6. Most forecasting problems involve a time series:

7. Many business applications of forecasting utilize daily, weekly, monthly, quarterly, or annual data, but any reporting interval may be used.
The data may be instantaneous, such as the viscosity of a chemical product at the point in time where it is measured; it may be cumulative, such as the total sales of a product during the month; or it may be a statistic that in some way reflects the activity of the variable during the time period, such as the daily closing price of a specific stock on the New York Stock Exchange.

8. The reason that forecasting is so important is that prediction of future events is a critical input into many types of planning and decision making processes, with application to areas such as the following:
Operations Management. Business organizations routinely use forecasts of product sales or demand for services in order to schedule production, control inventories, manage the supply chain, determine staffing requirements, and plan capacity. Forecasts may also be used to determine the mix of products or services to be offered and the locations at which products are to be produced.

10. Introduction to Time Series Analysis and Forecasting, 2008 MJK
Chapter 1
Introduction to Time Series Analysis and Forecasting, MJK

11. Two broad types of methods:
Quantitative forecasting methods (focus of this course)
Makes formal use of historical data
A mathematical/statistical model
Past patterns are modeled and projected into the future
Qualitative forecasting methods (Chapter 4)
Subjective
Little available data (new product introduction)
Expert opinion often used
The Delphi method

12. Quantitative Forecasting Methods
Graphical and exploratory methods
Chapters 2 and 3
Regression methods
Chapter 5
Smoothing methods
Chapters 6 and 7
Formal time series analysis methods
Chapters 8 and 9
Supervised learning methods (time permitting)
Chapter 11 – neural networks, random forests, etc.

13. Terminology Point forecast or point estimate
Single estimate of future values of the response in a time series, e.g. next month’s sales.
Prediction or forecast interval
Interval estimate for a future value of the response in a time series. This interval should have a high chance of covering the yet unobserved value, e.g. 80% or 95%.
Forecast horizon or lead time How far out do we need forecasts for? e.g. next month, each month in the next year, next 4 years?

14. Examples of time series: Uncorrelated data, constant or stationary process model

15. Autocorrelated time series

16. Trend

17. Cyclic or seasonal time series

18. Nonstationary time series
Note: The previous three examples were also nonstationary time series.

19. Another nonstationary time series

20. A mixture of patterns - nonstationary

21. Cyclic patterns of different magnitudes – again nonstationary

22. Atypical events or Anomalies

23. The Forecasting Process

24. The Forecasting Process
Forecasting: Principles and Practice (5 steps)

25. The Forecasting Process
Forecasting: Principles and Practice (5 steps)

26. Software
There are numerous software packages that will allow us to analyze time series and make forecasts.
R/R-Studio – open source! Constantly evolving with a huge user community. Lots of internet resources!
JMP – SAS product has fairly substantial time series capabilities. We will use it some in this course.
Others: MINITAB, SPSS, Stata, MATLAB, etc.

27. More Examples of Time Series – What features do you see?

28. More Examples of Time Series – What features do you see?

29. More Examples of Time Series – What features do you see?
Monthly Sales Fastenal Corporation (01/01/04 – 12/31/2013)

30. More Examples of Time Series – What features do you see?
Monthly U.S. Liquor Sales in Millions of Dollars ( )

31. More Examples of Time Series – What features do you see?
Original Time Series
Plot of log10(Liquor Sales)

32. More Examples of Time Series – What features do you see?
Original Time Series

33. For class next time: Read FPP Chapter 1 – Getting Started
Download and install R from CRAN
Download and install R-Studio
Install R packages: forecast and fpp2
Work through all of the examples in the R Markdown file for Chapter 1 – Getting Started.
Install JMP 13 Pro from the WSU network.