Halilİbrahim BayrakdaroğluHalilİbrahim BayrakdaroğluDokuz Eylül UniversityDokuz Eylül University

Industrial Engineering DepartmentIndustrial Engineering Department

FORECASTING AND TIME SERIES

An ardent supporter of the hometown team should go to a game prepared to take

offense,no matter what happens

-Robert Benchley

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.

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.

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.

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?

Facts in ForecastingMain 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.

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.Competitive advantage or firm'sCompetitors incorporate into their products and process)

Demand Forecasting

Predicts the quantity and timing of demand for a firm's products

Forecast HorizonRange Horizon Applications Methods

Long <5 years Facility PlanningCapacity planningProduct Plannig

EconomicDemographicMarket InformationTechnology

Intermediate 1 season-2 years Staffing PlansAggregateProduction Plan

Time seriesRegression

Short 1 day-1year PurchasingDetailed Job Scheduling

Trend ExplorationGraphical MethodsExponential Smoothing

Forecasting ApproachesForecasting Approaches

Qualitative MethodsQualitative Methods Used when situation is Used when situation is

vague & little data existvague & little data exist New products

New technology

Involve intuition, experience e.g., forecasting sales on

Internet

Quantitative MethodsQuantitative Methods

Forecasting ApproachesForecasting Approaches

Qualitative MethodsQualitative Methods Used when situation is Used when situation is

vague & little data existvague & little data exist New productsNew products

New technologyNew technology

Involve intuition, experienceInvolve intuition, experience e.g., forecasting sales on e.g., forecasting sales on

InternetInternet

Quantitative MethodsQuantitative Methods Used when situation Used when situation

is ‘stable’ & historical is ‘stable’ & historical data existdata exist

Existing products

Current technology

Involve mathematical techniques

e.g., forecasting sales of color televisions

Quantitative Forecasting MethodsQuantitative Forecasting Methods

Quantitative

Forecasting

Quantitative Forecasting Methods

Quantitative

Forecasting

Time Series

Models

Quantitative Forecasting Methods

Causal

Models

Quantitative

Forecasting

Time Series

Models

Quantitative Forecasting Methods

Causal

Models

Quantitative

Forecasting

Time Series

Models

Exponential

Smoothing

Trend

Models

Moving

Average

Quantitative Forecasting Methods

Causal

Models

Quantitative

Forecasting

Time Series

Models

RegressionExponential

Smoothing

Trend

Models

Moving

Average

Quantitative Forecasting Methods

Causal

Models

Quantitative

Forecasting

Time Series

Models

RegressionExponential

Smoothing

Trend

Models

Moving

Average

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.

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 .

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.

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...

Time Series Components

Time Series Components

TrendTrend

Time Series Components

TrendTrend CyclicalCyclical

Time Series Components

TrendTrend

SeasonalSeasonal

CyclicalCyclical

Time Series Components

TrendTrend

SeasonalSeasonal

CyclicalCyclical

IrregularIrregular

The Components of a Time SeriesTrend 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

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.Mo., Qtr., Yr.

ResponseResponse

Cycle

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.Mo., Qtr.

ResponseResponse

SummerSummer

© 1984-1994 T/Maker Co.

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.

You may have to fight a battle more than once to win it

-Margaret Thatcher