Example 16.7a Deseasonalizing: The Ratio-to-Moving-Averages Method.

Slides:

Advertisements

Similar presentations

Example 16.7b Estimating Seasonality with Regression.

Advertisements

Example 16.1 Forecasting Sales at Best Chips. Thomson/South-Western 2007 © South-Western/Cengage Learning © 2009Practical Management Science, Revised.

Forecasting Models With Linear Trend. Linear Trend Model If a modeled is hypothesized that has only linear trend and random effects, it will be of the.

Guide to Using Excel 2007 For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 8th Ed. Chapter 16: Analyzing.

19- 1 Chapter Nineteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.

Exponential Smoothing Methods

Moving Averages Ft(1) is average of last m observations

Forecasting Demand ISQA 511 Dr. Mellie Pullman.

Analyzing and Forecasting Time Series Data

1 Pertemuan 23 Deret Berkala, Peramalan, dan Angka Indeks-1 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1.

Session 10a. Decision Models -- Prof. Juran2 Overview Forecasting Methods Exponential Smoothing –Simple –Trend (Holt’s Method) –Seasonality (Winters’

Exercise 7.1 a. Find and report the four seasonal factors for quarter 1, 2, 3 and 4 sn1 = 1.191, sn2 = 1.521, sn3 = 0.804, sn4 = b. What is the.

1 Time Series Analysis Thanks to Kay Smith for making these slides available to me!

Forecasting Purpose is to forecast, not to explain the historical pattern Models for forecasting may not make sense as a description for ”physical” beaviour.

ForecastingModelsWith Trend and Seasonal Effects.

Example 16.8 Forecasting Quarterly Soft Drink Sales.

T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.

Modeling Time Series Data Module 5. A Composite Model We can fit a composite model of the form: Sales = (Trend) * (Seasonality) * (Cyclicality) * (Error)

Time Series and Forecasting

1 BABS 502 Moving Averages, Decomposition and Exponential Smoothing Revised March 11, 2011.

ISQA 459/559 Advanced Forecasting Mellie Pullman.

Winter’s Exponential smoothing

ENGM 745 Forecasting for Business & Technology Paula Jensen South Dakota School of Mines and Technology, Rapid City 3rd Session 2/01/12: Chapter 3 Moving.

Time Series and Forecasting

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

Chapter 4 Forecasting Mike Dohan BUSI Forecasting What is forecasting? Why is it important? In what areas can forecasting be applied?

Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.

Example 13.1 Forecasting Monthly Stereo Sales Testing for Randomness.

Time Series Analysis Introduction Averaging Trend Seasonality.

1 1 Slide © 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Chapter © 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or.

McGraw-Hill/Irwin Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. 3-2 Business Forecasting with Accompanying Excel-Based ForecastX™

Holt’s exponential smoothing

Example 16.7 Exponential Smoothing | 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7a | 16.7b16.1a a16.7a16.7b.

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

Forecasting Models Decomposition and Exponential Smoothing.

Example 16.6 Forecasting Hardware Sales at Lee’s.

Example 16.7 Forecasting Quarterly Sales at a Pharmaceutical Company.

INTERNAL ACHIEVEMENT STANDARD 3 CREDITS Time Series 1.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

Example 13.6a Houses Sold in the Midwest Exponential Smoothing.

Time series Model assessment. Tourist arrivals to NZ Period is quarterly.

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Example 2.9 Time Series Plots | 2.2 | 2.3 | 2.4 | 2.5 | 2.6 | 2.7 | 2.8 | 2.10 | 2.11 | TOYS.XLS n Lists.

Example 13.6 Houses Sold in the Midwest Moving Averages.

Example 16.6 Regression-Based Trend Models | 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.2a | 16.7 | 16.7a | 16.7b16.1a a16.7.

T T18-07 Seasonally Adjusted Linear Trend Forecast Purpose Allows the analyst to create and analyze a "Seasonally Adjusted Linear Trend" forecast.

Economics 173 Business Statistics Lecture 26 © Fall 2001, Professor J. Petry

COMPLETE BUSINESS STATISTICS

1 BABS 502 Moving Averages, Decomposition and Exponential Smoothing Revised March 14, 2010.

Time Series and Forecasting

Example 13.3 Quarterly Sales at Intel Regression-Based Trend Models.

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

Times Series Forecasting and Index Numbers Chapter 16 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Time Series and Forecasting Chapter 16.

Forecasting. ©2006 Pearson Prentice Hall — Introduction to Operations and Supply Chain Management — Bozarth & Handfield Chapter 9, Slide 2 Why Forecast?

T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Time Series and Forecasting Chapter 16.

Input dataClick ToolsClick Data Analysis Click Regression Enter range of data from Column B in Input Y Range Enter range of data from Column A in Input.

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

Forecasting Purpose is to forecast, not to explain the historical pattern Models for forecasting may not make sense as a description for ”physical” behaviour.

John Loucks St. Edward’s University . SLIDES . BY.

Chapter 4: Seasonal Series: Forecasting and Decomposition

Exponential Smoothing

Forecasting Models With Trend and Seasonal Effects

Today we are going back in time to help us predict the future!!!

Chap 4: Exponential Smoothing

Exponential Smoothing

Presentation transcript:

Example 16.7a Deseasonalizing: The Ratio-to-Moving-Averages Method

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b COCACOLA.XLS n We return to this data file that contains the sales history from 1986 to quarter 2 of n Is it possible to obtain the same forecast accuracy with the ratio-to-moving-averages method as we obtained with the Winters’ method?

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Ratio-to-Moving-Averages Method n There are many varieties of sophisticated methods for deseasonalizing time series data but they are all variations of the ratio-to-moving-averages method. n This method is applicable when we believe that seasonality is multiplicative. n The goal is to find the seasonal indexes, which can then be used to deseasonalize the data. n The method is not meant for hand calculations and is straightforward to implement with StatPro.

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Solution n The answer to the question posed earlier depends on which forecasting method we use to forecast the deseasonalized data. n The ratio-to-moving-averages method only provides a means for deseasonalizing the data and providing seasonal indexes. Beyond this, any method can be used to forecast the deseasonalized data, and some methods work better than others. n For this example, we will compare two methods: the moving averages method with a span of 4 quarters, and Holt’s exponential smoothing method optimized.

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Solution -- continued n Because the deseasonalized data still has a a clear upward trend, we would expect Holt’s method to do well and we would expect the moving averages forecasts to lag behind the trend. n This is exactly what occurred. n To implement the latter method in StatPro, we proceed exactly as before, but this time select Holt’s method and be sure to check “Use this deseasonalizing method”. We get a large selection of optional charts.

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Ration-to-Moving-Averages Output n This output shows the seasonal indexes from the ratio-to-moving-averages method. They are virtually identical to the indexes found using Winters’ method. n Here are the summary measures for forecast errors.

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Ratio-to-Moving Averages Output

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Forecast Plot of Deseasonalized Series n Here we see only the smooth upward trend with no seasonality, which Holt’s method is able to track very well.

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b The Results of Reseasonalizing

| 16.1a | 16.2 | 16.3 | 16.4 | 16.5 | 16.6 | 16.2a | 16.7 | 16.7b16.1a a b Summary Measures n The summary measures of forecast errors below are quite comparable to those from Winters’ method. n The reason is that both arrive at virtually the same seasonal pattern.