1 Energy Load Mining Using Univariate Time Series Analysis By: Taghreed Alghamdi & Ali Almadan 03/02/2015 Caruth Hall 0184
2 Energy Forecasting Energy Saving Energy consumption
3 Introduction: Energy consumption. Energy Forecasting. Energy Saving.
4 Part I Energy Load Consumption Analysis By: Taghreed Alghamdi
5 Energy Load Consumption Analysis
6 Data Preparation: Detect missing values. Many methods deal with missing values have been developed to detect missing values 1. Pre-replacing methods: They replace missing values before the data mining process. They use statistical methods and machine learning methods. 2. Embedded methods: They deal with missing values while doing data mining itself.
8 Time series: is a sequence of observations, which are ordered in time. There re methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. 1. Trend 2. Seasonal 3. Cyclical 4. Irregular
9 Trend: A trend exists when there is a long-term increase or decrease in the data. Trend can be linear or nonlinear such as exponential growth. Energy consumption Time
10 Seasonal: A seasonal exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week). Seasonality is always of a fixed and known period. Energy consumption Winter Summer Winter Spring Summer Fall Spring Fall Time (Quarterly)
11 Cyclical: A cyclical exists when data exhibit rises and falls that are not of fixed period. The duration of these fluctuations is usually of at least 2 years. 1 Cycle Energy consumption Year
12 How do we capture the time series we have?! Visual inspection. Augmented Dickey Fuller (ADF) test. Kwiatkowski Phillips Schmidt Shin (KPSS) test. Seasonality: osequence plot. oseasonal subseries plot. omultiple box plot. oautocorrelation plot
13 What if the time series data contain considerable error? Then, the first step in the process is using filtering and smoothing techniques. Smoothing techniques : Smoothing techniques used for reducing of canceling the effect due to random variation in time series data. This technique, when properly applied, provide a clearer view of the true underlying behavior of the time series we have. The most common techniques: 1- Exponential smoothing methods. 2- Moving average smoothing methods.
14 Exponential smoothing methods: Single Exponential Smoothing. Data show no trend or seasonality. Double Exponential Smoothing (Holt method). Data show only trend. Triple Exponential Smoothing (Holt-Winters method). Data show trend and seasonality.
15 Smoothing Objective: Select smoothing parameters that minimize the error over the historical data.
16 Single Exponential Smoothing: smoothing constant value 0 α 1 The closer smoothing constant value to 1, the more strongly the forecast depends upon recent values to 0.30 works well
20 Conclusion: Although nobody can really look into the future, modern statistical methods and data mining methods go along way in helping us to analysis the consumption and forecasting of energy. Exponential smoothing has proven through the years to be very useful in many forecasting situations, and a tool used to observe the data clearly.
21 Ready For Advanced Modeling
22 Part II Energy Load Forecasting By: Ali Almadan
24 Load Forecasting Categories: Short-Term Forecasting: The short term load forecasting predicts the load demand from one day to several weeks. This forecasting is needed to avoid overloading and to ensure security. Medium-Term Forecasting: The medium term load forecasting predicts the load demand from a month to several years. This forecasting is used when a company is planning to expand or shrink its operations in the next years. Long-Term Forecasting: The long term load forecasting predicts the demand from a year up to 20 years. This is usually used to plan for the power system network.
25 Techniques & Models: There are different models and techniques can be used for forecasting electricity load. Some of those are: - Neural Networks. - Holt-Winters Forecasting Model. - Autoregressive Integrated Moving Average ARIMA: - Non-seasonal ARIMA. - Seasonal ARIMA (Also called SARIMA) - Vector ARIMA (Also called VARIMA).
26 Models Classification: Univariate Models: Univariate Models are those forecasting models that are based on the description of only one variable. For example, using just energy consumption in MWh. Multivariate Models: Multivariate Models are those forecasting models that are based on the description of many variables to forecast energy consumption and demand. Example:
27 ARIMA: Autoregressive Integrated Moving Average model is a univariate time series model that is used to: 1) Understand the data. 2) Predict points in the future.
28 ARIMA: Representation: ARIMA is represented as ARIMA(p,d,q). ARIMA consists of three parts: AR I MA AR: Autoregressive (p) I: Integrated (d) MA: Moving Average (q)
29 ARIMA: AR Model AR: Autoregressive. Autoregressive (AR) models are models in which the value of a variable in one period is related to its values in previous periods. AR(q) is an autoregressive model with q lags:
30 ARIMA: MA Model MA: Moving Average. Moving Average (MA) models account for the possibility of a relationship between a variable and the residual from previous periods. MA(q) is a moving average model with q lags:
31 ARIMA: I Model To understand the I part, we need to explain a stationary process first. What is a stationary process? A stationary process has a mean and variance that do not change over time AND the process does not have trend. ARIMA model needs the data as stationary. What if we need to apply ARIMA and the data is not stationary? We can use differencing variable
32 ARIMA Stages: Box-Jenkins Methodology ARIMA modeling has three stages. Those stages are: - Identification. - Estimation and diagnostic checking. - Forecasting.
33 ARIMA Stages: Box-Jenkins Methodology ARIMA modeling has three stages. Those stages are: - Identification. - Estimation and diagnostic checking. - Forecasting.
34 ARIMA Stages: Identification Looking at the time series and try to identify its characteristics: Plot examination: - Are there outliers? - Are there missing values? - Is the time series stationary? - Do we need to perform any transformation? Autocorrelation Examination: - What kind of ACF do we have? - What kind of PACF do we have?
35 ARIMA Stages: Identification Looking at the time series and try to identify its characteristics: - Is it seasonal without a trned? Then use the moving average part to detrend it.
36 ARIMA Stages: Identification Looking at the time series and try to identify its characteristics: - Is it stationary? A stationary process has a mean and variance that do not change over time AND the process does not have trend.
37 ARIMA: Type of Inputs Stationary Non-stationary
38 ARIMA Stages: Box-Jenkins Methodology ARIMA modeling has three stages. Those stages are: - Identification. - Estimation and diagnostic checking. - Forecasting.
39 ARIMA Stages: Estimation In this stage, we estimate the different orders for different ARIMA models and we examine them. ARIMA(?,?,?) We need to estimate the autoregressive order, differencing order, and the moving average order. Rules: - High positive autocorrelation for a high number of lags often mean we need to use a differencing order more than 1.
40 ARIMA Stages: Estimation Rules: - If the first lag has a negative or zero autocorrelation, then we probably do not need a high differencing order. - If we use zero as differencing order, then we are dealing with stationary time series (e.g. ARIMA (1,0,1) ) - If we use one as differencing order, then we are dealing with a time series that has a constant average trend (e.g. ARIMA (1,1,1)) - If we use two as differencing order, then we are dealing with a time series that has a time-varying trend (e.g. ARIMA (1,2,1))
41 ARIMA Stages: Diagnostic Checking Diagnostic Checking: We check if the model fits well. We used the autocorrelation function ACF and the partial autocorrelation function PACF in the checking process. If yes, then the residuals of the model should resemble a white noise process. If no, we need to come up with better estimation to come up with a better model.
42 ARIMA Stages: Box-Jenkins Methodology ARIMA modeling has three stages. Those stages are: - Identification. - Estimation and diagnostic checking. - Forecasting.
43 ARIMA Stages: Forecasting The final part? ALMOST THERE! When we get a model that fits, it s time do the forecasting by running the model:
44 Evaluation The final model can be evaluated using one year testing set and Mean Absolute Percentage Error
45 Conclusion How does that help us save energy? Is it just saving energy?
46 Support Different programs support ARIMA: - R (tseries Package) - SAS (PROC ARIMA) - Microsoft Excel (With NumXL)
47 References: Derby, Nate. "Time Series Forecasting Methods - SAS Business Analytics and." By Nate Derby. Calgary SAS Users Group, n.d. Web. 02 Mar "FACTORS AFFECTING ELECTRICITY CONSUMPTION IN THE U.S." Innovation Electricity Efficiency (2013): Web. "FACTORS INFLUENCING ENERGY CONSUMPTION AND COSTS IN BROILER PROCESSING PLANTS IN THE SOUTH." SOUTHERN JOURNAL OF AGRICULTURAL ECONOMICS (1978): 1-6. Web. Fomby, Thomas B. "Exponential Smoothing Models1." Southern Methodist University. Department of Economics, n.d. Web. Gotham, Douglas J. "Energy Forecasting Methods." Energy Center. Purdue University, 15 Nov Web.
48 Mayilvahanan, M., and M. Sabitha. "Opportunities and Challenges in Using Renewable Resources in India: A Data Mining Approach." International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) 1.2 (2012): 1-4. Web. Mohamed*,, Hoda K., Soliman M. El-Debeiky, Hassan M. Mahmoud, and Khaled M. Destawy. "Data Mining for Electrical Load Forecasting In Egyptian Electrical Network." Ain Shams U., Faculty of Eng., Computer & System Eng. Dept., Cairo, Egypt (n.d.): Web Piasecki, Dave. "Exponential Smoothing Explained." InventoryOps. N.p., n.d. Web Zhu, Bo, Min-You Chen, Neal Wade, and Li Ran. "A Prediction Model for Wind Farm Power Generation Based on Fuzzy Modeling." Procedia Environmental Sciences 12 (2012):
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
TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 firstname.lastname@example.org. Introduction Time series (TS) data refers to observations
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi- 110 012 email@example.com 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
COMP6053 lecture: Time series analysis, autocorrelation firstname.lastname@example.org Time series analysis The basic idea of time series analysis is simple: given an observed sequence, how can we build a model that
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 email@example.com
APS 425 Fall 25 Time Series : ARIMA Models Instructor: G. William Schwert 585-275-247 firstname.lastname@example.org Topics Typical time series plot Pattern recognition in auto and partial autocorrelations
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
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
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
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
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
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
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),
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
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
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
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
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
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
TIME-SERIES ANALYSIS, MODELLING AND FORECASTING USING SAS SOFTWARE Ramasubramanian V. IA.S.R.I., Library Avenue, Pusa, New Delhi 110 012 email@example.com 1. Introduction Time series (TS) data refers
SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing firstname.lastname@example.org IN SPSS SESSION 2, WE HAVE LEARNT: Elementary Data Analysis Group Comparison & One-way
Advanced Forecasting Techniques and Models: ARIMA Short Examples Series using Risk Simulator For more information please visit: www.realoptionsvaluation.com or contact us at: email@example.com
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
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
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
Time series Forecasting using Holt-Winters Exponential Smoothing Prajakta S. Kalekar(04329008) Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract
ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM KATE GLEASON COLLEGE OF ENGINEERING John D. Hromi Center for Quality and Applied Statistics NEW (or REVISED) COURSE (KCOE-CQAS- 873 - Time Series Analysis
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
26 CHAPTER 2. TREND AND SEASONAL COMPONENTS 2.2 Elimination of Trend and Seasonality Here we assume that the TS model is additive and there exist both trend and seasonal components, that is X t = m t +
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.....................................
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,
Practical Time Series Analysis Using SAS Anders Milhøj Contents Preface... vii Part 1: Time Series as a Subject for Analysis... 1 Chapter 1 Time Series Data... 3 1.1 Time Series Questions... 3 1.2 Types
Regression and Time Series Analysis of Petroleum Product Sales in Masters Energy oil and Gas 1 Ezeliora Chukwuemeka Daniel 1 Department of Industrial and Production Engineering, Nnamdi Azikiwe University
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
Causal Leading Indicators Detection for Demand Forecasting Yves R. Sagaert, El-Houssaine Aghezzaf, Nikolaos Kourentzes, Bram Desmet Department of Industrial Management, Ghent University 13/07/2015 EURO
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
BUS 655/655/455 Forecasting & Predictive Analytics How to Predict the Future with Confidence Spring 2015 Wednesday 6:30-9:15, Room 208 Prof. Stephen P. Stuk, Ph.D. Office GBS 409 Mobile: 678-468-3452 use
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
Page 119 EMPIRICAL INVESTIGATION AND MODELING OF THE RELATIONSHIP BETWEEN GAS PRICE AND CRUDE OIL AND ELECTRICITY PRICES Morsheda Hassan, Wiley College Raja Nassar, Louisiana Tech University ABSTRACT Crude
ECMM703 Analysis and Computation for Finance Time Series - An Introduction Alejandra González Harrison 161 Email: firstname.lastname@example.org Time Series - An Introduction A time series is a sequence of observations
Simple Methods and Procedures Used in Forecasting The project prepared by : Sven Gingelmaier Michael Richter Under direction of the Maria Jadamus-Hacura What Is Forecasting? Prediction of future events
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
ScienceAsia 27 (2) : 27-278 Demand Forecasting and Production Planning for Highly Seasonal Demand Situations: Case Study of a Pressure Container Factory Pisal Yenradee a,*, Anulark Pinnoi b and Amnaj Charoenthavornying
Introduction to Time Series Analysis. Lecture 1. Peter Bartlett 1. Organizational issues. 2. Objectives of time series analysis. Examples. 3. Overview of the course. 4. Time series models. 5. Time series
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
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
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 email@example.com, 2 firstname.lastname@example.org and 3
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
! # % # & ())! +,,, # ) (. / 0 1 ) / 2 3 4 ) )/) 5 Hess & Polak 1 An Analysis of the Effects of Speed Limit Enforcement Cameras on Accident Rates Stephane Hess and John Polak Centre for Transport Studies
11. Time series and dynamic linear models Objective To introduce the Bayesian approach to the modeling and forecasting of time series. Recommended reading West, M. and Harrison, J. (1997). models, (2 nd
Communications for Statistical Applications and Methods 2015, Vol. 22, No. 6, 675 683 DOI: http://dx.doi.org/10.5351/csam.2015.22.6.675 Print ISSN 2287-7843 / Online ISSN 2383-4757 A Study on the Comparison
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
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
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
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
Instituto Superior Técnico (IST, Portugal) and CEMAT email@example.com European Summer School in Industrial Mathematics Universidad Carlos III de Madrid July 2013 Outline The objective of this short
Forecasting, Prediction Models, and Times Series Analysis with Oracle Business Intelligence and Analytics Rittman Mead BI Forum 2013 Dan Vlamis and Tim Vlamis Vlamis Software Solutions 816-781-2880 http://www.vlamis.com
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
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-
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
Smoothing methods Marzena Narodzonek-Karpowska Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel What Is Forecasting? Process of predicting a future event Underlying basis of all
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
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
Adaptive Demand-Forecasting Approach based on Principal Components Time-series an application of data-mining technique to detection of market movement Toshio Sugihara Abstract In this study, an adaptive
Forecasting sales and intervention analysis of durable products in the Greek market. Empirical evidence from the new car retail sector. Maria K. Voulgaraki Department of Economics, University of Crete,
An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50 B. Uma Devi 1 D.Sundar 2 and Dr. P. Alli 3 1 Assistant Professor, Department of Computer Science, R.D.Govt.
Sales forecasting # 2 Arthur Charpentier firstname.lastname@example.org 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting
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
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
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
State Space Time Series Analysis p. 1 State Space Time Series Analysis Siem Jan Koopman http://staff.feweb.vu.nl/koopman Department of Econometrics VU University Amsterdam Tinbergen Institute 2011 State
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.
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
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
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
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.
Exchange Rate Volatility Forecasting Using GARCH models in R Roger Roth Martin Kammlander Markus Mayer June 9, 2009 Agenda Preliminaries 1 Preliminaries Importance of ER Forecasting Predicability of ERs
Chapter 27 Using Predictor Variables Chapter Table of Contents LINEAR TREND...1329 TIME TREND CURVES...1330 REGRESSORS...1332 ADJUSTMENTS...1334 DYNAMIC REGRESSOR...1335 INTERVENTIONS...1339 TheInterventionSpecificationWindow...1339
Report of summer project Institute for development and research in banking technology 13 May -13 July, 2013 CASH DEMAND FORECASTING FOR ATMS Guided By Dr. Mahil Carr Associate Professor IDRBT, Hyderabad
Estimating and Forecasting Network Traffic Performance based on Statistical Patterns Observed in SNMP data. K. Hu 1,2, A. Sim 1, Demetris Antoniades 3, Constantine Dovrolis 3 1 Lawrence Berkeley National
Chapter 25 Specifying Forecasting Models Chapter Table of Contents SERIES DIAGNOSTICS...1281 MODELS TO FIT WINDOW...1283 AUTOMATIC MODEL SELECTION...1285 SMOOTHING MODEL SPECIFICATION WINDOW...1287 ARIMA
Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless the volume of the demand known. The success of the business
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)
CHAPTER 19 TIME SERIES ANALYSIS & FORECASTING Basic Concepts 1. Time Series Analysis BASIC CONCEPTS AND FORMULA The term Time Series means a set of observations concurring any activity against different