Time series data: each case represents a point in time. Each cell gives a value for each variable for each time period. Stationarity: Data are stationary.

Slides:

Advertisements

Similar presentations

Time Series Analysis Definition of a Time Series process

Advertisements

Chapter 9. Time Series From Business Intelligence Book by Vercellis Lei Chen, for COMP

DSCI 5340: Predictive Modeling and Business Forecasting Spring 2013 – Dr. Nick Evangelopoulos Exam 1 review: Quizzes 1-6.

Forecasting OPS 370.

4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis.

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

Time Series Building 1. Model Identification

Exponential Smoothing Methods

Time Series Analysis Autocorrelation Naive & Simple Averaging

Time series of the day. Stat Sept 2008 D. R. Brillinger Simple descriptive techniques Trend X t =  +  t +  t Filtering y t =  r=-q s a r.

MOVING AVERAGES AND EXPONENTIAL SMOOTHING

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

Forecasting & Time Series Minggu 6. Learning Objectives Understand the three categories of forecasting techniques available. Become aware of the four.

Quantitative Business Forecasting Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.

CHAPTER 4 MOVING AVERAGES AND SMOOTHING METHODS (Page 107)

Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.

Time Series Analysis and Index Numbers Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.

Copyright 2013 John Wiley & Sons, Inc. Chapter 8 Supplement Forecasting.

Statistical Forecasting Models

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

MOVING AVERAGES AND EXPONENTIAL SMOOTHING. Forecasting methods: –Averaging methods. Equally weighted observations –Exponential Smoothing methods. Unequal.

Statistics and Modelling 3.8 Credits: Internally Assessed.

Constant process Separate signal & noise Smooth the data: Backward smoother: At any give T, replace the observation yt by a combination of observations.

Applied Business Forecasting and Planning

Diane Stockton Trend analysis. Introduction Why do we want to look at trends over time? –To see how things have changed What is the information used for?

Winter’s Exponential smoothing

STAT 497 LECTURE NOTES 7 FORECASTING.

LSS Black Belt Training Forecasting. Forecasting Models Forecasting Techniques Qualitative Models Delphi Method Jury of Executive Opinion Sales Force.

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

The Forecast Process Dr. Mohammed Alahmed

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?

Introduction to Forecasting COB 291 Spring Forecasting 4 A forecast is an estimate of future demand 4 Forecasts contain error 4 Forecasts can be.

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

Forecasting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.

Copyright © 2012 Pearson Education, Inc. All rights reserved. Chapter 10 Introduction to Time Series Modeling and Forecasting.

Forecasting supply chain requirements

© 2004 Prentice-Hall, Inc. Chapter 7 Demand Forecasting in a Supply Chain Supply Chain Management (2nd Edition) 7-1.

Holt’s exponential smoothing

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.

FORECAST OF NEW DOMESTIC AUTO PRODUCTION MGT 267 Applied Business Forecasting Professor Mohsen Elhafsi Group 1: Hui Guo Minjia Xu Ao Gao.

PowerPoint presentation to accompany Chopra and Meindl Supply Chain Management, 5e 1-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall.

Definition of Time Series: An ordered sequence of values of a variable at equally spaced time intervals. The variable shall be time dependent.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

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

Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.

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

Forecasting is the art and science of predicting future events.

Forecasts and Projections “A trend is a trend is a trend, But the question is, will it bend? Will it alter its course Through some unforeseen force And.

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

Demand Forecasting Production and Operations Management Judit Uzonyi-Kecskés Research Assistant Department of Management and Corporate Economics Budapest.

Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.

Predicting Future. Two Approaches to Predition n Extrapolation: Use past experiences for predicting future. One looks for patterns over time. n Predictive.

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.

Financial Analysis, Planning and Forecasting Theory and Application

Time Series Analysis By Tyler Moore.

Demand Forecasting Production and Operations Management

What is Correlation Analysis?

Demand Forecasting Production and Operations Management

“The Art of Forecasting”

Forecasting Methods ISAT /10/2018.

Statistics and Modelling 3.8

Forecasting Elements of good forecast Accurate Timely Reliable

Exponential Smoothing

Chapter 8 Supplement Forecasting.

Final Project FIN 335 Unknown Students.

Chap 4: Exponential Smoothing

Presentation transcript:

Time series data: each case represents a point in time. Each cell gives a value for each variable for each time period. Stationarity: Data are stationary. A stationary process has the property that the mean, variance and autocorrelation structure do not change over time. Seasonality: By seasonality, we mean periodic fluctuations. The usage of time series models is: to obtain an understanding of underlying forces and structures that produce the observed data. to fit a model and proceed to forecasting and monitoring. Techniques: Exponential Smoothing ARIMA Models

Exponential smooting Exponential Smoothing Four available model types: Simple. The simple model assumes that the series has no tr end and no seasonal variation. Holt. The Holt model assumes that the series has a linear trend and no seasonal variation. Winters. The Winters model assumes that the series has a li near trend and multiplicative seasonal variation (its magnitude incre ases or decreases with the overall level of the series). Custom. A custom model allows you to specify the trend an d seasonality components.