Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London)
|
|
- August Warner
- 8 years ago
- Views:
Transcription
1 Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London) 1
2 Forecasting: definition Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed (Wikipedia) There are many categories of forecasting methods : Categorical vs Quantitative Naïve approach (forecast based solely on previous period realisation) Time Series (Box Jenkins Methodology) Judgemental methods (based on subjective probability) 2
3 Why Forecasting? Economic Forecasting (Inflation/GDP forecasting) Sales Forecasting Supply Chain Forecasting Earthquake Forecasting Weather Forecasting Hotel Management (Room bookings forecasting) 3
4 Forecasting medical time series : a sample of existing studies The Application of Forecasting Techniques to Modelling Emergency Medical System Calls in Calgary, Alberta [see Channouf et al (2006)] Box Jenkins Methodology in Medical Research [see Helfenstein (1996)] Time series modelling for syndromic surveillance [Reis and Mandle (2003)] Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, [See Solomon and Moran (2011)] 4
5 Today s application: UK Hospital Waiting Lists PM's election pledge in jeopardy as report reveals patients waiting 6% longer The Guardian NHS Chief warns of rising Hospital waiting times BBC News NHS waiting times may increase at one in three flagship hospitals: report The Telegraph We will match people's symptoms to certain groups of conditions and try to provide a general forecast Weather used to forecast illness Daily Mail Surgery waiting lists hit one million Mail online 5
6 Solution: Forecasting the number of patients placed on a waiting list! But, why is it so important? Waiting lists grow if the demand for a specific treatment outcasts hospital capacity (supply) Hence, forecasting the number of patient placed on a waiting list at a given time might provide an estimate of the demand and supply imbalance Hospital Managers, could in principle, know exactly by how much the list is growing/declining (at least within a confidence interval) This methodology could be easily extended to forecast individual hospital waiting lists, as well as treatments/surgery specific waiting lists 6
7 Data Available at : cs/performancedataandstatistics/hospitalwaitingtimesan dliststatistics/index.htm This dataset contains information on patients waiting to be admitted to NHS hospitals in England either as a day case or ordinary admission. Provider based Time series data from April 1998 to Feb 2010 (143 obs) It does not contain: Emergency cases and outpatients 7
8 Methodology ARIMA models have been found particularly useful in describing stationary (non seasonal) time series. A stationary stochastic process is a process whose joint distribution does not shift in time and space, therefore characterized by finite first and second order moments Wold s Theorem: Any stationary series can be expressed as a combination of two components: a perfectly forecastable series and a moving average of possibly infinite order. Thus non-seasonal series can always be approximated by a MA( ) model, which in turn can be approximated by an ARMA (p, q) with a small number of parameters p, q. 8
9 Methodology cont d However, most time series are not stationary and usually have a seasonal component! We have to transform these series into stationary non seasonal before we can model them Seasonal differencing Non - stationarity can be classified : Trend in mean (difference as many times as required) Trend in variance (apply a power transformation, e.g. log) Note: The latter should only be applied only if it stabilises the variance! 9
10 Total waiting list x 1000 patients totalwaitinglistx m1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 t Source: author s calculations 10
11 dlwsa 0.02 Growth Rate of Hospital Waiting Lists (difference the log) 1998m1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 t Source: author s calculations 11
12 Box Jenkins Methodology Plot the series and identify the trend (is the series trending in the mean/variance?) Test for stationarity (Augmented Dickey Fuller Test, KPSS Test, PP-Test) Transform the series into a stationary series (power transformation /seasonal differencing/ first differencing etc ) Plot the Autocorrelations and Partial Autocorrelation functions (Identify possible models) Estimate the possible models (check for coefficient significance and white noise residuals) Select the best models based on the information criteria (the model with lowest AIC, BIC, HQ ) Select the best two models given the above and test their forecasting accuracy (Diebold Mariano Test, Granger Newbold) 12
13 -0.50 Model identification: ACF, PACF Partial autocorrelations of dltwl Lag Bartlett's formula for MA(q) 95% confidence bands Lag 95% Confidence bands [se = 1/sqrt(n)] ACF = Correlation of the series and lag of itself across time r k = Corr (X t, X t -k ) = γ k /γ 0 PACF: Amount of correlation between a variable and a lag of itself that is not explained by correlations at all lower-order-lags 13
14 List of potential models AR (1) AR (2) MA (1) MA (2) ARMA (1, 1) ARMA (2, 1) ARMA (1, 2) Note: This was the model with : smallest information criteria, individually/jointly significant coefficients, uncorrelated residuals as well as yielding the most accurate forecasts! 14
15 Selecting the best Model The selected model is an ARMA ( 1, 2 ) dltwl = 0.90 dltwl (-1) (ε t 1 ) (ε t 2 ) The series shows an high degree of persistence insofar it inherits a large proportion of the past period realisation Invertibility, causality and stationary conditions: The process satisfies the stationarity condition since the coefficient on the AR component is in absolute value lesser than one, hence ensuring that the process has finite first and second moments The process satisfies invertibility and causality conditions since the roots of the characteristic equation of the autoregressive process and the moving average, lie outside the unit circle. These last two requirements imply that the model is uniquely identified in its parameters! 15
16 FORECASTING 16
17 ARMA (1,2) Model forecast Out of sample forecast 1998m1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 t dlwsa xb prediction, one-step 17
18 No change forecast 1998m1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 t dltwl L.dltwl 18
19 Forecasting Diagnostics Theil U-statistics U-statistics = 1 T 1 T T 1 t T 1 t f t+1 y t+1 y t yt+1 yt yt 2 2 = 0.34 If U< 1 the model is superior to a No change forecast If U>1 the model is inferior to a No change forecast 19
20 Drawbacks of this approach Lack of data availability It assumes that the Data Generating Process is time invariant (however it might well not be the case!) The point forecast confidence interval becomes wider and wider as the forecasting horizon increases! (Unless we are only concerned with one step ahead forecasts and the data-set is sufficiently large for our purposes) It is very unlikely to find a model that fits particularly well the data, in fact, in practice models can often explain very little! 20
21 Conclusions The past values of a variable contain very important information about the future of that variable! Time Series Analysis Forecasting is a very useful tool and it is easily implementable It can be applied to a variety of fields of which one one of them is Healthcare /Medical Research In this example, we have seen that only by looking at the time series properties of the data we were able to infer the sign of the growth rate of hospital admissions! 21
22 To conclude, if your model fails 22
TIME SERIES ANALYSIS
TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 lmb@iasri.res.in. Introduction Time series (TS) data refers to observations
More informationTIME SERIES ANALYSIS
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi- 110 012 ram_stat@yahoo.co.in 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
More informationSome useful concepts in univariate time series analysis
Some useful concepts in univariate time series analysis Autoregressive moving average models Autocorrelation functions Model Estimation Diagnostic measure Model selection Forecasting Assumptions: 1. Non-seasonal
More informationUnivariate Time Series Analysis; ARIMA Models
Econometrics 2 Spring 25 Univariate Time Series Analysis; ARIMA Models Heino Bohn Nielsen of4 Outline of the Lecture () Introduction to univariate time series analysis. (2) Stationarity. (3) Characterizing
More informationRob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1
Rob J Hyndman Forecasting using 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Outline 1 Regression with ARIMA errors 2 Example: Japanese cars 3 Using Fourier terms for seasonality 4
More informationTime Series - ARIMA Models. Instructor: G. William Schwert
APS 425 Fall 25 Time Series : ARIMA Models Instructor: G. William Schwert 585-275-247 schwert@schwert.ssb.rochester.edu Topics Typical time series plot Pattern recognition in auto and partial autocorrelations
More informationUnivariate Time Series Analysis; ARIMA Models
Econometrics 2 Fall 25 Univariate Time Series Analysis; ARIMA Models Heino Bohn Nielsen of4 Univariate Time Series Analysis We consider a single time series, y,y 2,..., y T. We want to construct simple
More informationForecasting areas and production of rice in India using ARIMA model
International Journal of Farm Sciences 4(1) :99-106, 2014 Forecasting areas and production of rice in India using ARIMA model K PRABAKARAN and C SIVAPRAGASAM* Agricultural College and Research Institute,
More informationTime Series Analysis
Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos
More informationForecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model
Tropical Agricultural Research Vol. 24 (): 2-3 (22) Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model V. Sivapathasundaram * and C. Bogahawatte Postgraduate Institute
More informationUSE OF ARIMA TIME SERIES AND REGRESSORS TO FORECAST THE SALE OF ELECTRICITY
Paper PO10 USE OF ARIMA TIME SERIES AND REGRESSORS TO FORECAST THE SALE OF ELECTRICITY Beatrice Ugiliweneza, University of Louisville, Louisville, KY ABSTRACT Objectives: To forecast the sales made by
More informationITSM-R Reference Manual
ITSM-R Reference Manual George Weigt June 5, 2015 1 Contents 1 Introduction 3 1.1 Time series analysis in a nutshell............................... 3 1.2 White Noise Variance.....................................
More informationSales forecasting # 2
Sales forecasting # 2 Arthur Charpentier arthur.charpentier@univ-rennes1.fr 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting
More informationPromotional Forecast Demonstration
Exhibit 2: Promotional Forecast Demonstration Consider the problem of forecasting for a proposed promotion that will start in December 1997 and continues beyond the forecast horizon. Assume that the promotion
More informationTime Series Analysis
JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),
More informationA Trading Strategy Based on the Lead-Lag Relationship of Spot and Futures Prices of the S&P 500
A Trading Strategy Based on the Lead-Lag Relationship of Spot and Futures Prices of the S&P 500 FE8827 Quantitative Trading Strategies 2010/11 Mini-Term 5 Nanyang Technological University Submitted By:
More informationUnivariate and Multivariate Methods PEARSON. Addison Wesley
Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston
More informationHow To Model A Series With Sas
Chapter 7 Chapter Table of Contents OVERVIEW...193 GETTING STARTED...194 TheThreeStagesofARIMAModeling...194 IdentificationStage...194 Estimation and Diagnostic Checking Stage...... 200 Forecasting Stage...205
More informationAnalysis of algorithms of time series analysis for forecasting sales
SAINT-PETERSBURG STATE UNIVERSITY Mathematics & Mechanics Faculty Chair of Analytical Information Systems Garipov Emil Analysis of algorithms of time series analysis for forecasting sales Course Work Scientific
More informationUsing JMP Version 4 for Time Series Analysis Bill Gjertsen, SAS, Cary, NC
Using JMP Version 4 for Time Series Analysis Bill Gjertsen, SAS, Cary, NC Abstract Three examples of time series will be illustrated. One is the classical airline passenger demand data with definite seasonal
More informationPITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU
PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard
More informationAdvanced Forecasting Techniques and Models: ARIMA
Advanced Forecasting Techniques and Models: ARIMA Short Examples Series using Risk Simulator For more information please visit: www.realoptionsvaluation.com or contact us at: admin@realoptionsvaluation.com
More informationTime Series Analysis: Basic Forecasting.
Time Series Analysis: Basic Forecasting. As published in Benchmarks RSS Matters, April 2015 http://web3.unt.edu/benchmarks/issues/2015/04/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD jonathan.starkweather@unt.edu
More informationStudying Achievement
Journal of Business and Economics, ISSN 2155-7950, USA November 2014, Volume 5, No. 11, pp. 2052-2056 DOI: 10.15341/jbe(2155-7950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us
More informationThreshold Autoregressive Models in Finance: A Comparative Approach
University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC) - Conference Papers Faculty of Informatics 2011 Threshold Autoregressive Models in Finance: A Comparative
More informationEnergy Load Mining Using Univariate Time Series Analysis
Energy Load Mining Using Univariate Time Series Analysis By: Taghreed Alghamdi & Ali Almadan 03/02/2015 Caruth Hall 0184 Energy Forecasting Energy Saving Energy consumption Introduction: Energy consumption.
More informationAnalysis and Computation for Finance Time Series - An Introduction
ECMM703 Analysis and Computation for Finance Time Series - An Introduction Alejandra González Harrison 161 Email: mag208@exeter.ac.uk Time Series - An Introduction A time series is a sequence of observations
More informationLecture 4: Seasonal Time Series, Trend Analysis & Component Model Bus 41910, Time Series Analysis, Mr. R. Tsay
Lecture 4: Seasonal Time Series, Trend Analysis & Component Model Bus 41910, Time Series Analysis, Mr. R. Tsay Business cycle plays an important role in economics. In time series analysis, business cycle
More informationPredicting Indian GDP. And its relation with FMCG Sales
Predicting Indian GDP And its relation with FMCG Sales GDP A Broad Measure of Economic Activity Definition The monetary value of all the finished goods and services produced within a country's borders
More informationSearch Marketing Cannibalization. Analytical Techniques to measure PPC and Organic interaction
Search Marketing Cannibalization Analytical Techniques to measure PPC and Organic interaction 2 Search Overview How People Use Search Engines Navigational Research Health/Medical Directions News Shopping
More informationTime Series Analysis
Time Series Analysis hm@imm.dtu.dk Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby 1 Outline of the lecture Identification of univariate time series models, cont.:
More informationPrediction of Stock Price usingautoregressiveintegrated Moving AverageFilter Arima P,D,Q
Global Journal of Science Frontier Research Mathematics and Decision Sciences Volume 13 Issue 8 Version 1.0 Year Type : Double Blind Peer Reviewed International Research Journal Publisher: Global Journals
More informationMGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal
MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims
More information1 Short Introduction to Time Series
ECONOMICS 7344, Spring 202 Bent E. Sørensen January 24, 202 Short Introduction to Time Series A time series is a collection of stochastic variables x,.., x t,.., x T indexed by an integer value t. The
More informationCOMP6053 lecture: Time series analysis, autocorrelation. jn2@ecs.soton.ac.uk
COMP6053 lecture: Time series analysis, autocorrelation jn2@ecs.soton.ac.uk Time series analysis The basic idea of time series analysis is simple: given an observed sequence, how can we build a model that
More informationForecasting the US Dollar / Euro Exchange rate Using ARMA Models
Forecasting the US Dollar / Euro Exchange rate Using ARMA Models LIUWEI (9906360) - 1 - ABSTRACT...3 1. INTRODUCTION...4 2. DATA ANALYSIS...5 2.1 Stationary estimation...5 2.2 Dickey-Fuller Test...6 3.
More informationIBM SPSS Forecasting 22
IBM SPSS Forecasting 22 Note Before using this information and the product it supports, read the information in Notices on page 33. Product Information This edition applies to version 22, release 0, modification
More informationGraphical Tools for Exploring and Analyzing Data From ARIMA Time Series Models
Graphical Tools for Exploring and Analyzing Data From ARIMA Time Series Models William Q. Meeker Department of Statistics Iowa State University Ames, IA 50011 January 13, 2001 Abstract S-plus is a highly
More information9th Russian Summer School in Information Retrieval Big Data Analytics with R
9th Russian Summer School in Information Retrieval Big Data Analytics with R Introduction to Time Series with R A. Karakitsiou A. Migdalas Industrial Logistics, ETS Institute Luleå University of Technology
More informationTime Series Analysis 1. Lecture 8: Time Series Analysis. Time Series Analysis MIT 18.S096. Dr. Kempthorne. Fall 2013 MIT 18.S096
Lecture 8: Time Series Analysis MIT 18.S096 Dr. Kempthorne Fall 2013 MIT 18.S096 Time Series Analysis 1 Outline Time Series Analysis 1 Time Series Analysis MIT 18.S096 Time Series Analysis 2 A stochastic
More informationLecture 2: ARMA(p,q) models (part 3)
Lecture 2: ARMA(p,q) models (part 3) Florian Pelgrin University of Lausanne, École des HEC Department of mathematics (IMEA-Nice) Sept. 2011 - Jan. 2012 Florian Pelgrin (HEC) Univariate time series Sept.
More informationTime Series Analysis
Time Series Analysis Autoregressive, MA and ARMA processes Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 212 Alonso and García-Martos
More informationReaders will be provided a link to download the software and Excel files that are used in the book after payment. Please visit http://www.xlpert.
Readers will be provided a link to download the software and Excel files that are used in the book after payment. Please visit http://www.xlpert.com for more information on the book. The Excel files are
More informationForecasting model of electricity demand in the Nordic countries. Tone Pedersen
Forecasting model of electricity demand in the Nordic countries Tone Pedersen 3/19/2014 Abstract A model implemented in order to describe the electricity demand on hourly basis for the Nordic countries.
More informationPerforming Unit Root Tests in EViews. Unit Root Testing
Página 1 de 12 Unit Root Testing The theory behind ARMA estimation is based on stationary time series. A series is said to be (weakly or covariance) stationary if the mean and autocovariances of the series
More informationThe SAS Time Series Forecasting System
The SAS Time Series Forecasting System An Overview for Public Health Researchers Charles DiMaggio, PhD College of Physicians and Surgeons Departments of Anesthesiology and Epidemiology Columbia University
More informationChapter 9: Univariate Time Series Analysis
Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of
More informationForecasting Using Eviews 2.0: An Overview
Forecasting Using Eviews 2.0: An Overview Some Preliminaries In what follows it will be useful to distinguish between ex post and ex ante forecasting. In terms of time series modeling, both predict values
More informationPromotional Analysis and Forecasting for Demand Planning: A Practical Time Series Approach Michael Leonard, SAS Institute Inc.
Promotional Analysis and Forecasting for Demand Planning: A Practical Time Series Approach Michael Leonard, SAS Institute Inc. Cary, NC, USA Abstract Many businesses use sales promotions to increase the
More informationMonitoring the SARS Epidemic in China: A Time Series Analysis
Journal of Data Science 3(2005), 279-293 Monitoring the SARS Epidemic in China: A Time Series Analysis Dejian Lai The University of Texas and Jiangxi University of Finance and Economics Abstract: In this
More informationTHE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Homework Assignment #2
THE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Homework Assignment #2 Assignment: 1. Consumer Sentiment of the University of Michigan.
More informationIs the Forward Exchange Rate a Useful Indicator of the Future Exchange Rate?
Is the Forward Exchange Rate a Useful Indicator of the Future Exchange Rate? Emily Polito, Trinity College In the past two decades, there have been many empirical studies both in support of and opposing
More informationJOHANNES TSHEPISO TSOKU NONOFO PHOKONTSI DANIEL METSILENG FORECASTING SOUTH AFRICAN GOLD SALES: THE BOX-JENKINS METHODOLOGY
DOI: 0.20472/IAC.205.08.3 JOHANNES TSHEPISO TSOKU North West University, South Africa NONOFO PHOKONTSI North West University, South Africa DANIEL METSILENG Department of Health, South Africa FORECASTING
More informationTime Series Analysis
Time Series Analysis Forecasting with ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos (UC3M-UPM)
More informationTraffic Safety Facts. Research Note. Time Series Analysis and Forecast of Crash Fatalities during Six Holiday Periods Cejun Liu* and Chou-Lin Chen
Traffic Safety Facts Research Note March 2004 DOT HS 809 718 Time Series Analysis and Forecast of Crash Fatalities during Six Holiday Periods Cejun Liu* and Chou-Lin Chen Summary This research note uses
More informationTime Series Analysis and Forecasting
Time Series Analysis and Forecasting Math 667 Al Nosedal Department of Mathematics Indiana University of Pennsylvania Time Series Analysis and Forecasting p. 1/11 Introduction Many decision-making applications
More informationIntegrated Resource Plan
Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 650-962-9670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1
More informationChapter 4: Vector Autoregressive Models
Chapter 4: Vector Autoregressive Models 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie IV.1 Vector Autoregressive Models (VAR)...
More informationIBM SPSS Forecasting 21
IBM SPSS Forecasting 21 Note: Before using this information and the product it supports, read the general information under Notices on p. 107. This edition applies to IBM SPSS Statistics 21 and to all
More informationSoftware Review: ITSM 2000 Professional Version 6.0.
Lee, J. & Strazicich, M.C. (2002). Software Review: ITSM 2000 Professional Version 6.0. International Journal of Forecasting, 18(3): 455-459 (June 2002). Published by Elsevier (ISSN: 0169-2070). http://0-
More informationDo Electricity Prices Reflect Economic Fundamentals?: Evidence from the California ISO
Do Electricity Prices Reflect Economic Fundamentals?: Evidence from the California ISO Kevin F. Forbes and Ernest M. Zampelli Department of Business and Economics The Center for the Study of Energy and
More informationADVANCED FORECASTING MODELS USING SAS SOFTWARE
ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting
More informationTIME-SERIES ANALYSIS, MODELLING AND FORECASTING USING SAS SOFTWARE
TIME-SERIES ANALYSIS, MODELLING AND FORECASTING USING SAS SOFTWARE Ramasubramanian V. IA.S.R.I., Library Avenue, Pusa, New Delhi 110 012 ramsub@iasri.res.in 1. Introduction Time series (TS) data refers
More informationForecasting Tourism Demand: Methods and Strategies. By D. C. Frechtling Oxford, UK: Butterworth Heinemann 2001
Forecasting Tourism Demand: Methods and Strategies By D. C. Frechtling Oxford, UK: Butterworth Heinemann 2001 Table of Contents List of Tables List of Figures Preface Acknowledgments i 1 Introduction 1
More informationAnalysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model
Science Journal of Applied Mathematics and Statistics 2015; 3(2): 47-57 Published online March 28, 2015 (http://www.sciencepublishinggroup.com/j/sjams) doi: 10.11648/j.sjams.20150302.14 ISSN: 2376-9491
More information3.1 Stationary Processes and Mean Reversion
3. Univariate Time Series Models 3.1 Stationary Processes and Mean Reversion Definition 3.1: A time series y t, t = 1,..., T is called (covariance) stationary if (1) E[y t ] = µ, for all t Cov[y t, y t
More information5. Multiple regression
5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful
More informationI. Introduction. II. Background. KEY WORDS: Time series forecasting, Structural Models, CPS
Predicting the National Unemployment Rate that the "Old" CPS Would Have Produced Richard Tiller and Michael Welch, Bureau of Labor Statistics Richard Tiller, Bureau of Labor Statistics, Room 4985, 2 Mass.
More informationANALYSIS OF EUROPEAN, AMERICAN AND JAPANESE GOVERNMENT BOND YIELDS
Applied Time Series Analysis ANALYSIS OF EUROPEAN, AMERICAN AND JAPANESE GOVERNMENT BOND YIELDS Stationarity, cointegration, Granger causality Aleksandra Falkowska and Piotr Lewicki TABLE OF CONTENTS 1.
More informationVector Time Series Model Representations and Analysis with XploRe
0-1 Vector Time Series Model Representations and Analysis with plore Julius Mungo CASE - Center for Applied Statistics and Economics Humboldt-Universität zu Berlin mungo@wiwi.hu-berlin.de plore MulTi Motivation
More informationISSUES IN UNIVARIATE FORECASTING
ISSUES IN UNIVARIATE FORECASTING By Rohaiza Zakaria 1, T. Zalizam T. Muda 2 and Suzilah Ismail 3 UUM College of Arts and Sciences, Universiti Utara Malaysia 1 rhz@uum.edu.my, 2 zalizam@uum.edu.my and 3
More informationTime-Series Analysis CHAPTER. 18.1 General Purpose and Description 18-1
CHAPTER 8 Time-Series Analysis 8. General Purpose and Description Time-series analysis is used when observations are made repeatedly over 5 or more time periods. Sometimes the observations are from a single
More informationAR(p) + MA(q) = ARMA(p, q)
AR(p) + MA(q) = ARMA(p, q) Outline 1 3.4: ARMA(p, q) Model 2 Homework 3a Arthur Berg AR(p) + MA(q) = ARMA(p, q) 2/ 12 ARMA(p, q) Model Definition (ARMA(p, q) Model) A time series is ARMA(p, q) if it is
More informationTHE SVM APPROACH FOR BOX JENKINS MODELS
REVSTAT Statistical Journal Volume 7, Number 1, April 2009, 23 36 THE SVM APPROACH FOR BOX JENKINS MODELS Authors: Saeid Amiri Dep. of Energy and Technology, Swedish Univ. of Agriculture Sciences, P.O.Box
More informationDiscrete Time Series Analysis with ARMA Models
Discrete Time Series Analysis with ARMA Models Veronica Sitsofe Ahiati (veronica@aims.ac.za) African Institute for Mathematical Sciences (AIMS) Supervised by Tina Marquardt Munich University of Technology,
More informationTime Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents
Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents Prasanna Desikan and Jaideep Srivastava Department of Computer Science University of Minnesota. @cs.umn.edu
More information16 : Demand Forecasting
16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationTime Series Analysis
Time Series 1 April 9, 2013 Time Series Analysis This chapter presents an introduction to the branch of statistics known as time series analysis. Often the data we collect in environmental studies is collected
More informationNTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling
1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information
More informationJetBlue Airways Stock Price Analysis and Prediction
JetBlue Airways Stock Price Analysis and Prediction Team Member: Lulu Liu, Jiaojiao Liu DSO530 Final Project JETBLUE AIRWAYS STOCK PRICE ANALYSIS AND PREDICTION 1 Motivation Started in February 2000, JetBlue
More informationChapter 1. Vector autoregressions. 1.1 VARs and the identi cation problem
Chapter Vector autoregressions We begin by taking a look at the data of macroeconomics. A way to summarize the dynamics of macroeconomic data is to make use of vector autoregressions. VAR models have become
More informationModeling and forecasting regional GDP in Sweden. using autoregressive models
MASTER THESIS IN MICRODATA ANALYSIS Modeling and forecasting regional GDP in Sweden using autoregressive models Author: Haonan Zhang Supervisor: Niklas Rudholm 2013 Business Intelligence Program School
More informationCh.3 Demand Forecasting.
Part 3 : Acquisition & Production Support. Ch.3 Demand Forecasting. Edited by Dr. Seung Hyun Lee (Ph.D., CPL) IEMS Research Center, E-mail : lkangsan@iems.co.kr Demand Forecasting. Definition. An estimate
More informationApplication of Seasonal Box-Jenkins Techniques for Modelling Monthly Internally Generated Revenue of Rivers State of Nigeria
Application of Seasonal Box-Jenkins Techniques for Modelling Monthly Internally Generated Revenue of Rivers State of Nigeria Ette Harrison Etuk 1, Innocent Uchenna Amadi 2, Igboye Simon Aboko 3, Mazi Yellow
More informationThe information content of lagged equity and bond yields
Economics Letters 68 (2000) 179 184 www.elsevier.com/ locate/ econbase The information content of lagged equity and bond yields Richard D.F. Harris *, Rene Sanchez-Valle School of Business and Economics,
More informationChapter 5. Analysis of Multiple Time Series. 5.1 Vector Autoregressions
Chapter 5 Analysis of Multiple Time Series Note: The primary references for these notes are chapters 5 and 6 in Enders (2004). An alternative, but more technical treatment can be found in chapters 10-11
More informationForecasting Stock Market Series. with ARIMA Model
Journal of Statistical and Econometric Methods, vol.3, no.3, 2014, 65-77 ISSN: 2241-0384 (print), 2241-0376 (online) Scienpress Ltd, 2014 Forecasting Stock Market Series with ARIMA Model Fatai Adewole
More informationTime Series Analysis in Economics. Klaus Neusser
Time Series Analysis in Economics Klaus Neusser May 26, 2015 Contents I Univariate Time Series Analysis 3 1 Introduction 1 1.1 Some examples.......................... 2 1.2 Formal definitions.........................
More informationAPPLICATION OF THE VARMA MODEL FOR SALES FORECAST: CASE OF URMIA GRAY CEMENT FACTORY
APPLICATION OF THE VARMA MODEL FOR SALES FORECAST: CASE OF URMIA GRAY CEMENT FACTORY DOI: 10.2478/tjeb-2014-0005 Ramin Bashir KHODAPARASTI 1 Samad MOSLEHI 2 To forecast sales as reliably as possible is
More informationFORECAST MODEL USING ARIMA FOR STOCK PRICES OF AUTOMOBILE SECTOR. Aloysius Edward. 1, JyothiManoj. 2
FORECAST MODEL USING ARIMA FOR STOCK PRICES OF AUTOMOBILE SECTOR Aloysius Edward. 1, JyothiManoj. 2 Faculty, Kristu Jayanti College, Autonomous, Bengaluru. Abstract There has been a growing interest in
More informationForecasting the PhDs-Output of the Higher Education System of Pakistan
Forecasting the PhDs-Output of the Higher Education System of Pakistan Ghani ur Rehman, Dr. Muhammad Khalil Shahid, Dr. Bakhtiar Khan Khattak and Syed Fiaz Ahmed Center for Emerging Sciences, Engineering
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationTime Series in Mathematical Finance
Instituto Superior Técnico (IST, Portugal) and CEMAT cnunes@math.ist.utl.pt European Summer School in Industrial Mathematics Universidad Carlos III de Madrid July 2013 Outline The objective of this short
More informationAgenda. Managing Uncertainty in the Supply Chain. The Economic Order Quantity. Classic inventory theory
Agenda Managing Uncertainty in the Supply Chain TIØ485 Produkjons- og nettverksøkonomi Lecture 3 Classic Inventory models Economic Order Quantity (aka Economic Lot Size) The (s,s) Inventory Policy Managing
More informationApplication of ARIMA models in soybean series of prices in the north of Paraná
78 Application of ARIMA models in soybean series of prices in the north of Paraná Reception of originals: 09/24/2012 Release for publication: 10/26/2012 Israel José dos Santos Felipe Mestrando em Administração
More informationA model to predict client s phone calls to Iberdrola Call Centre
A model to predict client s phone calls to Iberdrola Call Centre Participants: Cazallas Piqueras, Rosa Gil Franco, Dolores M Gouveia de Miranda, Vinicius Herrera de la Cruz, Jorge Inoñan Valdera, Danny
More informationTime Series Analysis of Aviation Data
Time Series Analysis of Aviation Data Dr. Richard Xie February, 2012 What is a Time Series A time series is a sequence of observations in chorological order, such as Daily closing price of stock MSFT in
More informationARMA, GARCH and Related Option Pricing Method
ARMA, GARCH and Related Option Pricing Method Author: Yiyang Yang Advisor: Pr. Xiaolin Li, Pr. Zari Rachev Department of Applied Mathematics and Statistics State University of New York at Stony Brook September
More informationTime Series Analysis
Time Series Analysis Lecture Notes for 475.726 Ross Ihaka Statistics Department University of Auckland April 14, 2005 ii Contents 1 Introduction 1 1.1 Time Series.............................. 1 1.2 Stationarity
More information