Time-Series Forecasting Learning Objectives 1.Describe What Forecasting Is 2. Forecasting Methods 3.Explain Time Series & Components 4.Smooth a Data.

Slides:

Advertisements

Similar presentations

“The Art of Forecasting”

Advertisements

Forecasting OPS 370.

© 1997 Prentice-Hall, Inc. S2 - 1 Principles of Operations Management Forecasting Chapter S2.

Operations Management Forecasting Chapter 4

Bina Nusantara Model Ramalan Peretemuan 13: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008.

Trends and Seasonality Using Multiple Regression with Time Series Data Many time series data have a common tendency of growing over time, and therefore.

PRODUCTION AND OPERATIONS MANAGEMENT

Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

Qualitative Forecasting Methods

Operations Management

Operations Management Forecasting Chapter 4

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J Operations Management Forecasting Chapter 4.

Chapter 5 Forecasting. What is Forecasting Forecasting is the scientific methodology for predicting what will happen in the future based on the data in.

4 Forecasting PowerPoint presentation to accompany Heizer and Render

Basic Business Statistics (9th Edition)

Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Slides 13b: Time-Series Models; Measuring Forecast Error

© 2003 Prentice-Hall, Inc.Chap 12-1 Business Statistics: A First Course (3 rd Edition) Chapter 12 Time-Series Forecasting.

Lecture 4 Time-Series Forecasting

© 2002 Prentice-Hall, Inc.Chap 13-1 Statistics for Managers using Microsoft Excel 3 rd Edition Chapter 13 Time Series Analysis.

Time Series “The Art of Forecasting”. What Is Forecasting? Process of predicting a future event Underlying basis of all business decisions –Production.

Datta Meghe Institute of Management Studies Quantitative Techniques Unit No.:04 Unit Name: Time Series Analysis and Forecasting 1.

Chapter 15 Demand Management & Forecasting

The Importance of Forecasting in POM

Forecasting. What is Forecasting? Process of predicting a future event Underlying basis of all business decisions: Production Inventory Personnel Facilities.

Production Planning and Control. 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models.

CHAPTER 3 FORECASTING.

CLASS B.Sc.III PAPER APPLIED STATISTICS. Time Series “The Art of Forecasting”

© 2006 Prentice Hall, Inc.4 – 1 Forcasting © 2006 Prentice Hall, Inc. Heizer/Render Principles of Operations Management, 6e Operations Management, 8e.

Operations Management

Business Forecasting Used to try to predict the future Uses two main methods: Qualitative – seeking opinions on which to base decision making – Consumer.

Chapter 5 Demand Forecasting.

Chapter 4 Forecasting Production Planning Overview  What is forecasting?  Types of forecasts  7 steps of forecasting  Qualitative forecasting.

Forecasting Professor Ahmadi.

MBA.782.ForecastingCAJ Demand Management Qualitative Methods of Forecasting Quantitative Methods of Forecasting Causal Relationship Forecasting Focus.

Demand Management and Forecasting Module IV. Two Approaches in Demand Management Active approach to influence demand Passive approach to respond to changing.

1 1 Slide Forecasting Professor Ahmadi. 2 2 Slide Learning Objectives n Understand when to use various types of forecasting models and the time horizon.

Forecasting. 預測 (Forecasting) A Basis of Forecasting In business, forecasts are the basis for budgeting and planning for capacity, sales, production and.

10B11PD311 Economics. Process of predicting a future event on the basis of past as well as present knowledge and experience Underlying basis of all business.

1 Chapter 13 Forecasting  Demand Management  Qualitative Forecasting Methods  Simple & Weighted Moving Average Forecasts  Exponential Smoothing  Simple.

© 1999 Prentice-Hall, Inc. Chap Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series  Moving Averages  Exponential.

Production and Operations Management Forecasting session II Predicting the future demand Qualitative forecast methods  Subjective Quantitative.

1 Chapter 5 Demand Forecasting. 2 1.Importance of Forecasting  Helps planning for long-term growth  Helps in gauging the economic activity (auto sales,

Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.

Time Series Analysis and Forecasting. Introduction to Time Series Analysis A time-series is a set of observations on a quantitative variable collected.

Learning Objectives Describe what forecasting is Explain time series & its components Smooth a data series –Moving average –Exponential smoothing Forecast.

The Aim of Forecasting The aim of forecasting is to reduce the risk or uncertainty that the firm faces in its short-term operational decision making and.

Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 16-1 Chapter 16 Time-Series Forecasting and Index Numbers Basic Business Statistics 11 th.

MGS3100_03.ppt/Feb 11, 2016/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Time Series Forecasting Feb 11, 2016.

4 - 1 Course Title: Production and Operations Management Course Code: MGT 362 Course Book: Operations Management 10 th Edition. By Jay Heizer & Barry Render.

Module: Forecasting Operations Management as a Competitive Weapon.

Forecasting is the art and science of predicting future events.

13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.

Assignable variation Deviations with a specific cause or source. forecast bias or assignable variation or MSE? Click here for Hint.

4-1 Operations Management Forecasting Chapter Learning Objectives When you complete this chapter, you should be able to : Identify or Define :

Welcome to MM305 Unit 5 Seminar Dr. Bob Forecasting.

Welcome to MM305 Unit 5 Seminar Forecasting. What is forecasting? An attempt to predict the future using data. Generally an 8-step process 1.Why are you.

Yandell – Econ 216 Chap 16-1 Chapter 16 Time-Series Forecasting.

4 Forecasting Demand PowerPoint presentation to accompany

Time Series Forecasts Trend - long-term upward or downward movement in data. Seasonality - short-term fairly regular variations in data related to factors.

Demand Management and Forecasting

Statistics for Managers using Microsoft Excel 3rd Edition

“The Art of Forecasting”

Module 2: Demand Forecasting 2.

assignable variation Deviations with a specific cause or source.

Prepared by Lee Revere and John Large

“Measures of Trend” Dr. A. PHILIP AROKIADOSS Chapter 1 Time Series

TIME SERIES MODELS – MOVING AVERAGES.

Presentation transcript:

Time-Series Forecasting

Learning Objectives 1.Describe What Forecasting Is 2. Forecasting Methods 3.Explain Time Series & Components 4.Smooth a Data Series 5.Forecast Using Smoothing Methods, & Trend 6.Use MAD to Measure Forecast Error

What Is Forecasting? 1.Process of Predicting a Future Event 2.Underlying Basis of All Business Planning Production Inventory Personnel Facilities Sales will be $200 Million!

Forecasting Methods 1.Qualitative Methods Expert Opinion Delphi Method Surveys 2.Quantitative Methods Time Series Causal Regression

Quantitative Forecasting Steps 1.Select Several Forecasting Methods 2.‘Forecast’ the Past 3.Evaluate Forecasts 4.Select Best Method 5.Forecast the Future 6.Monitor Continuously Forecast Accuracy

What’s a Time Series? 1.Set of Numerical Data 2.Obtained by Observing Response Variable at Regular Time Periods 3.Assumes that Factors Influencing Past & Present Will Continue 4.Example Year: Sales:

Time Series Components Trend Seasonal Cyclical Irregular

Trend Component 1. Persistent, Overall Upward or Downward Pattern 2. Due to Population, Technology, etc to 20 Years Duration Mo., Qtr., Yr. Response

Linear Increasing Trend Linear Decreasing Trend Nonlinear TrendNo Trend Examples of Some Time Series Trend Patterns

Toaster Sales in Hundreds, By Quarter, TIME QUARTER1 QUARTER2 QUARTER3 QUARTER

Long-Term Trend in Toaster Sales

Cyclical Component 1. Repeating Up & Down Movements 2. Due to Interactions of Factors Influencing Economy 3. Usually 2-15 Years Duration Mo., Qtr., Yr. Response Cycle Prosperity Recession Depression Recovery

Cycles in Toaster Sales

Seasonal Component 1. Regular Pattern of Up & Down Fluctuations 2. Due to Weather, Customs,etc. 3. Occurs Within 1 Year Mo., Qtr. Response Summer

The Seasonal Pattern of Toaster Sales

Irregular Component l 1.Erratic, Unsystematic, ‘Residual’ Fluctuations l 2.Due to Random Variation or Unforeseen Events n Union Strike n Tornado l 3.Short Duration & Nonrepeating

Irregular Fluctuations in Toaster Sales Quarters

Multiplicative Time-Series Model l 1.Any Observed Value in a Time Series Is the Product of Time Series Components l 2.If Annual Data n Y = T x C x I l 3.If Quarterly or Monthly Data n Y i = T x S x C x I

Time Series Forecasting Linear Time Series Forecasting Trend? Smoothing Methods Trend Models YesNo Exponential Smoothing QuadraticExponential Holt- Winters Auto- Regressive Moving Average

Moving Average Method 1.Series of Arithmetic Means 2.Used Only for Smoothing Provides Overall Impression of Data Over Time 3.Equation L = Averaging Period (Odd # Years) MA (L) Y L i it    (L-1)/2 T=(1-L)/2

Time Response Y i Moving Total ( L =3) Moving Avg ( L =3) 19914NA = 1515/3 = = 1414/3 = = 1515/3 = = 1616/3 = NA Moving Average Calculation

Moving Average Graph Year Sales

Moving Average with Even Number of Periods

Time Series Forecasting Linear Time Series Forecasting Trend? Smoothing Methods Trend Models YesNo Exponential Smoothing QuadraticExponential Holt- Winters Auto- Regressive Moving Average

Exponential Smoothing Method l 1.Form of Weighted Moving Average n Weights Decline Exponentially n Most Recent Data Weighted Most l 2.Used for Smoothing & Forecasting n Assumes No Trend l 3.Requires Smoothing Coefficient (W) n Subjectively Chosen n Ranges from 0 to 1

Exponential Smoothing Equations l 1.Smoothing Equations E i = W·Y i + (1 - W)·E i-1 l 2.Forecasting Equation Y i+1 = E i E i = Smoothed Value Y i = Actual Value W = Smoothing Coefficient

Time Y i Smoothed Value, E i ( W =.2) Forecast Y i NA 19926(.2)(6) + (1-.2)(4.0) = (.2)(5) + (1-.2)(4.4) = (.2)(3) + (1-.2)(4.5) = (.2)(7) + (1-.2)(4.2) = NA 4.8 Exponential Smoothing Calculation ^ E i = W·Y i + (1 - W)·E i-1

Exponential Smoothing Graph Year Sales

Exponential Smoothing Thinking Challenge l You’re an economist for GM. You want to get a feel for the long-term trend in car sales. You want to smooth cyclical & random fluctuations using exponential smoothing with W =.25. Yearly sales (million units) are 2, 4, 1, 3.

To obtain starting values: 1.E 1 = Y 1 = 2 l 2.E 2 = W·Y 2 + (1 - W)·E 1 = (.25)(4) + ( )(2) = 2.5 l 3.E 3 = W·Y 3 + (1 - W)·E 2 = (.25)(1) + ( )(2.5) = l 4.E 4 = W·Y 4 + (1 - W)·E 3 l = (.25)(3)+( )(2.125)=2.34 Exponential Smoothing Solution*

Selecting Smoothing Coefficient (W) l 1.Subjectively Chosen n Computer Search Routines Available l 2.To Smooth Cyclical & Irregular, Small W n Reveals Long-Term Pattern l 3.To Forecast, Large W n Forecast Will Reflect Prior Period Data Most l 4.Recent Data Weighted Most for All W