1. Halilİbrahim Bayrakdaroğlu Dokuz Eylül University Industrial Engineering Department FORECASTING AND TIME SERIES

2. An ardent supporter of the hometown team should go to a game prepared to take offense,no matter what happens -Robert Benchley

3. Forecasting Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation for some variable of interest at some specified future date.Also,forecasting can be broadly considered as a method or a technique for estimating many future aspects of a business or other operation.

4. Forecasting is the process by which companies ponder and prepare for the future. It involves predicting the future outcome of various business decisions. This includes the future of the business as a whole, the future of an existing or proposed product or product line, and the future of the industry in which the business operates, to name a few. This helps the company prepare for the future. It also helps the organization make plans that will lead to becoming a financially successful business.A time series is a sequence of observations which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent.

5. Why forecasting ? Forecasting lays a ground for reducing the risk in all decision making because many of the decisions need to be made under uncertainty. In business applications,forecasting serves as a starting point of major decisions in finance,marketing,productions,and purchasing.

6. Key questions which must be answered: What is the purpose of the forecast? What specifically do we wish to forecast? How important is the past in predicting the future? What system will be used to make the forecast?

7. Facts in Forecasting Main assumption:Past pattern repeats itself into the future. Forecasts are rarely perfect:Don't expect forecasts to be exactly equal to the actual data. The science and art of forecasting try to minimize,but not to eliminate,forecast errors.Forecast errors mean the difference between actual and forecasted values. Forecasts for a group of products are usually more accurate than these for individual products;shorter period tend to be more accurate. Computer and IT are critical parts of the modern forecasting in large corporations.

8. Major Areas of Forecasting Economic Forecasting Predicts what the general business conditions will be in the future(Eg. Inflation rates,Gross National Product,Tax,Level of employment) Technology Forecasting Predicts the probality and / or possible future developments in technology(Eg.Competitiv e advantage or firm's Competitors incorporate into their products and process) Demand Forecasting Predicts the quantity and timing of demand for a firm's products

9. Forecast Horizon RangeHorizon Applications Methods Long<5 years Facility Planning Capacity planning Product Plannig Economic Demographic Market Information Technology Intermediate 1 season-2 years Staffing Plans Aggregate Production Plan Time series Regression Short 1 day-1year Purchasing Detailed Job Scheduling Trend Exploration Graphical Methods Exponential Smoothing

10. Forecasting Approaches Qualitative Methods Used when situation is vague & little data exist Used when situation is vague & little data exist  New products  New technology Involve intuition, experience e.g., forecasting sales on Internet Quantitative Methods

11. Forecasting Approaches Qualitative Methods Used when situation is vague & little data exist Used when situation is vague & little data exist  New products  New technology Involve intuition, experience Involve intuition, experience e.g., forecasting sales on Internet e.g., forecasting sales on Internet Quantitative Methods Used when situation is ‘stable’ & historical data exist Used when situation is ‘stable’ & historical data exist  Existing products  Current technology Involve mathematical techniques e.g., forecasting sales of color televisions

12. Quantitative Forecasting Methods Quantitative Forecasting

13. Quantitative Forecasting Methods Quantitative Forecasting Time Series Models

14. Quantitative Forecasting Methods Causal Models Quantitative Forecasting Time Series Models

15. Quantitative Forecasting Methods Causal Models Quantitative Forecasting Time Series Models Exponential Smoothing Trend Models Moving Average

16. Quantitative Forecasting Methods Causal Models Quantitative Forecasting Time Series Models Regression Exponential Smoothing Trend Models Moving Average

17. Quantitative Forecasting Methods Causal Models Quantitative Forecasting Time Series Models Regression Exponential Smoothing Trend Models Moving Average

18. Time Series and Time Series Methods By reviewing historical data over time, we can better understand the pattern of past behavior of a variable and better predict the future behavior. A time series is a set of observations on a variable measured over successive points in time or over successive periods of time. The objective of time series methods is to discover a pattern in the historical data and then extrapolate the pattern into the future. The forecast is based solely on past values of the variable and/or past forecast errors.

19. In statistics,signal processing, economics and mathemical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a to forecast future events based on known past events: to predict data points before they are measured. An example of time series forecasting in is predicting the opening price of a based on its past performance. Time series are very frequently plotted via.

20. Applications: The usage of time series models is twofold:  Obtain an understanding of the underlying forces and structure that produced the observed data  Fit a model and proceed to forecasting, monitoring or even feedback and feedforward control.

21. Time Series Analysis is used for many applications such as:  Economic Forecasting  Sales Forecasting  Budgetary Analysis  Stock Market Analysis  Yield Projections  Process and Quality Control  Inventory Studies  Workload Projections  Utility Studies  Census Analysis and many, many more...

22. Time Series Components

23. Trend

24. TrendCyclical

25. Trend Seasonal Cyclical

26. Trend Seasonal Cyclical Irregular

27. The Components of a Time Series Trend Component  It represents a gradual shifting of a time series to relatively higher or lower values over time.  Trend is usually the result of changes in the population, demographics,technology, and/or consumer preferences. Sales Time Upward trend

28. The Components of a Time Series Cyclical Component  It represents any recurring sequence of points above and below the trend line lasting more than one year.  We assume that this component represents multiyear cyclical movements in the economy. Mo., Qtr., Yr. Response Cycle

29. The Components of a Time Series Seasonal Component  It represents any repeating pattern, less than one year in duration, in the time series.  The pattern duration can be as short as an hour, or even less. Mo., Qtr. Response Summer © 1984-1994 T/Maker Co.

30. The Components of a Time Series Irregular Component  It is the “catch-all” factor that accounts for the deviation of the actual time series value from what we would expect based on the other components.  It is caused by the short-term,unanticipated,and nonrecurring factors that affect the time series.

31. You may have to fight a battle more than once to win it -Margaret Thatcher