TIME SERIES ANALYSIS & FORECASTING
|
|
|
- Jack Ferguson
- 10 years ago
- Views:
Transcription
1 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 periods of time. In order to describe this flow of economic activity, the statistician uses a time series. 2. Examples of Time Series Data Following are few examples of time series data: a) Profits earned by a company for each of the past five years. b) Workers employed by a company for each of the past 15 years. c) Number of students registered for CA examination in the institute for the past five years. d) The weekly wholesale price index for each of the past 30 week. e) Number of fatal road accidents in Delhi for each day for the past two months. 3. Components of a Time Series: A time series may contain one or more of the following four components: 1. Secular trend (T): (Long term trend) It is relatively consistent movement of a variable over a long period. 2. Seasonal variation (S): Variability of data due to seasonal influence. 3. Cyclical variation (C): Recurring sequence of points above and below the trend line lasting over more than one year. 4. Irregular variation (I): (random movements) Variations due residual factors that accounts for deviations of the actual time series values from those expected, given the effect of trend, seasonal and seasonal components. Example, erratic movements that do not have any pattern and are usually caused by unpredictable reason like earthquake, fire etc.
2 Advanced Management Accounting 4. Approaches for the Relationship amongst Components of a Time Series There are two approaches for the relationship amongst these components. (a) (b) 5. Trend Y = T S C I (multiplicative model) Y = T+S+C+I (additive model) Note: In multiplicative models S,C and I indexes are expressed as decimal percents Where Y is the result of the four components. The trend is the long-term movement of a time series. Any increase or decrease in the values of a variable occurring over a period of several years gives a trend. If the values of a variables remain statutory over several years, then no trend can be observed in the time series. 6. Methods of Fitting a Straight Line to a Time Series i. Free hand method, ii. iii. iv. The method of semi-averages, The method of moving averages The method of least squares. 7. Methods of Finding Short Period Variations Other Methods of finding short period variations 7.1 Simple Average: Simple Average: The method is very simple: average the data by months or quarters or years and them calculate the average for the period. Then find out, what percentage it is to the grand average. Seasonal Index = MonthlyorQuaterlyAverage GrandAverageofthemonthsorthequaters x100 Same results are obtained if the totals of each month or each quarter are obtained instead of the average of each month or each quarter. 7.2 Ratio-to-Trend Method This method is an improvement over the previous method because this assumes that seasonal variation for a given month is a constant fraction of trend. This method presumably isolates the seasonal factor in the following manner: 19.2
3 Time Series Analysis & Forecasting S C I= T S C I 8. Deseasonalization T Random elements (I) are supposed to disappear when the ratios are averaged. Further, a carefully selected period of years used in computation is expected to eliminate the influence of cyclical fluctuations (C). The process of eliminating seasonal fluctuations or deseasonalization of data consists of dividing each value in the original series by the corresponding value of the seasonal index. 9. Forecasting Time series forecasting methods involve the projection of future values of a variable based entirely on the past and present observation of that variable. 10. Various Forecasting Methods Using Time Series Mean Forecast The simplest forecasting method in which for the time period t. we forecast the value of the series to be equal to the mean of the series. This method is not adequate as trend effects and the cyclical effects are not taken into account in this Naïve forecast In this method, by taking advantage of the fact that there may be high correlation between successive pairs of values in a time series, we forecast the value, for the time period t, to-be equal to the actual value observed in the previous period t that is, time period (t 1): y t = yt Linear Trend Forecast In this method, a linear relationship between the time and the response value has been found from the linear relationship. yt = a+ bx where X will be found from the value of t and a and b are constants Non-linear Trend Forecast In this method, a non-linear relationship between the time and the response value has been found again by least-squares method. Then the value, for the 19.3
4 Advanced Management Accounting Question 1 time period t, will be calculated from the non-linear equation. i.e., y = a+ bx + cx t 2 where X-value will be calculated from the value of t Forecasting will Exponential Smoothing In this method, the forecast value for the time period t is found using exponential smoothing of time series. Specifically, at the time period t. y = y + α( y y ) t t 1 t t 1 where the forecasted value for time period t + 1 ; y t-1 = the forecasted value for time period t.: y t =the observed value for time period t. What is trend? What are the various methods of fitting a straight line to a time series? Trend is the long term movement of a time series. Any increase or decrease in the values of a variable occurring over a period of several years gives a trend. The various methods of fitting a straight line to a time series are: (i) Free hand method. (ii) The method of semi-averages. (iii) The method of moving averages. (iv) The method of least squares. Question 2 Name the various methods of fitting a straight line to a time series and briefly explain any two of them. The various methods of fitting a straight line are: (i) Free hand method (ii) Semi-average (iii) Moving average (iv) Least square 19.4
5 Time Series Analysis & Forecasting Freehand method: First the time series figures are plotted on a graph. The points are joined by straight lines. We get fluctuating straight lines, through which an average straight line is drawn. This method is however, inaccurate, since different persons may fit different trend lines for the same set of data. Method of Semi Averages: The given time series is divided into two parts, preferably with the same number of years. The average of each part is calculated and then a trend line through these averages is filled. Moving Average Method: A regular periodic cycle is identified in the time series. The moving average of n years is got by dividing the moving total by n. The method is also used for seasonal and cyclical variation. Method of Least Squares: The equation of a straight line is Y = A + b X, where X is the time period, say year and Y is the value of the item measured against time, a is the Y intercept and b, the co-efficient of X, indicating the slope of the line. To find a and b, the following normal equations are solved. Y = an + b X XY = a X + b X² Where n is the no. of observation in the series or n = no. of data items. Question 3 Apply the method of link relatives to the following data and calculate seasonal indices. Quarterly Figures Quarter I II III IV Calculation of seasonal indices by the method of link relatives. 19.5
6 Advanced Management Accounting Arithmetic average = = = = = Chain relatives 100 = = = Corrected chain relatives = = = Seasonal indices = = = = The calculation in the above table are explained below: Chain relative of the first quarter (on the basis of first quarter = 100) 19.6
7 Time Series Analysis & Forecasting Chain relative of the first quarter (on the basis of the last quarter) = = The difference between these chain relatives = = Difference per quarter = = Adjusted chain relatives are obtained by subtracting , , from the chain relatives of the 2 nd, 3 rd and 4 th quarters respectively. Average of corrected chain relatives = = = Correct chain relatives Seasonal variation index = Question 4 The following table relates to the tourist arrivals during 1990 to 1996 in India: Years : Tourists arrivals: (in millions) Fit a straight line trend by the method of least squares and estimates the number of tourists that would arrives in the year Fitting straight line Trend by the Method of Least squire 19.7
8 Advanced Management Accounting N = 7 Σy = 168 Σx = 0 Σxy = 53 Σx 2 = 28 The equation of the straight line trend is: Y = a + bx y 168 Since x = 0,a = = = 24 N 7 xy 53 And b = = = x 28 Hence Y = x Estimated Number of tourists that would arrive in 2000 Y = (7) = = million. 19.8
9 Time Series Analysis & Forecasting EXERCISE Question 1 Below are given the figures of production (in thousand quintals) of a sugar factory. (i) (ii) (iii) Year Production (thousand quintals) Fit a straight line by the 'least squares' method and tabulate the trend values. Eliminate the trend. What components of the series are thus left over? What is monthly increase in the production of sugar? (i) (ii) (iii) equation of straight line trend is Y = X After eliminating the trend we are left with cyclical and irregular variations. The monthly increase in the production of sugar is Question 2 b/12, i.e / 12 = thousand quintal. Calculate 5 yearly and 7 yearly moving averages for the following data of the numbers of commercial and industrial failure in a country during 1987 to Year No. of failures
10 Advanced Management Accounting Also plot the actual and trend values on a graph. Calculation of 5 yearly and 7 yearly moving Averages Year 5 yearly moving average 7 yearly moving average
Simple Methods and Procedures Used in Forecasting
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
Regression Analysis: A Complete Example
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
Example: Boats and Manatees
Figure 9-6 Example: Boats and Manatees Slide 1 Given the sample data in Table 9-1, find the value of the linear correlation coefficient r, then refer to Table A-6 to determine whether there is a significant
CALL VOLUME FORECASTING FOR SERVICE DESKS
CALL VOLUME FORECASTING FOR SERVICE DESKS Krishna Murthy Dasari Satyam Computer Services Ltd. This paper discusses the practical role of forecasting for Service Desk call volumes. Although there are many
TIME SERIES ANALYSIS. A time series is essentially composed of the following four components:
TIME SERIES ANALYSIS A time series is a sequence of data indexed by time, often comprising uniformly spaced observations. It is formed by collecting data over a long range of time at a regular time interval
Time series forecasting
Time series forecasting 1 The latest version of this document and related examples are found in http://myy.haaga-helia.fi/~taaak/q Time series forecasting The objective of time series methods is to discover
Section A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I
Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA - Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting
Outline: Demand Forecasting
Outline: Demand Forecasting Given the limited background from the surveys and that Chapter 7 in the book is complex, we will cover less material. The role of forecasting in the chain Characteristics of
Part 2: Analysis of Relationship Between Two Variables
Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable
16 : 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
Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless
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
8. Time Series and Prediction
8. Time Series and Prediction Definition: A time series is given by a sequence of the values of a variable observed at sequential points in time. e.g. daily maximum temperature, end of day share prices,
Industry Environment and Concepts for Forecasting 1
Table of Contents Industry Environment and Concepts for Forecasting 1 Forecasting Methods Overview...2 Multilevel Forecasting...3 Demand Forecasting...4 Integrating Information...5 Simplifying the Forecast...6
Time Series and Forecasting
Chapter 16 LEARNING OBJECTIVES When you have completed this chapter, you will be able to: 1 Define the components of a time series. 2 Determine a linear trend equation. 3 Compute a moving average. 4 Compute
CHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression
Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the
4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4
4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Non-linear functional forms Regression
Scatter Plot, Correlation, and Regression on the TI-83/84
Scatter Plot, Correlation, and Regression on the TI-83/84 Summary: When you have a set of (x,y) data points and want to find the best equation to describe them, you are performing a regression. This page
A Primer on Forecasting Business Performance
A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.
Slides Prepared by JOHN S. LOUCKS St. Edward s University
s Prepared by JOHN S. LOUCKS St. Edward s University 2002 South-Western/Thomson Learning 1 Chapter 18 Forecasting Time Series and Time Series Methods Components of a Time Series Smoothing Methods Trend
Theory at a Glance (For IES, GATE, PSU)
1. Forecasting Theory at a Glance (For IES, GATE, PSU) Forecasting means estimation of type, quantity and quality of future works e.g. sales etc. It is a calculated economic analysis. 1. Basic elements
Introduction to time series analysis
Introduction to time series analysis Margherita Gerolimetto November 3, 2010 1 What is a time series? A time series is a collection of observations ordered following a parameter that for us is time. Examples
Production Planning. Chapter 4 Forecasting. Overview. Overview. Chapter 04 Forecasting 1. 7 Steps to a Forecast. What is forecasting?
Chapter 4 Forecasting Production Planning MRP Purchasing Sales Forecast Aggregate Planning Master Production Schedule Production Scheduling Production What is forecasting? Types of forecasts 7 steps of
Section 1.5 Linear Models
Section 1.5 Linear Models Some real-life problems can be modeled using linear equations. Now that we know how to find the slope of a line, the equation of a line, and the point of intersection of two lines,
Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500 6 8480
1) The S & P/TSX Composite Index is based on common stock prices of a group of Canadian stocks. The weekly close level of the TSX for 6 weeks are shown: Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500
The importance of graphing the data: Anscombe s regression examples
The importance of graphing the data: Anscombe s regression examples Bruce Weaver Northern Health Research Conference Nipissing University, North Bay May 30-31, 2008 B. Weaver, NHRC 2008 1 The Objective
Linear Regression. Chapter 5. Prediction via Regression Line Number of new birds and Percent returning. Least Squares
Linear Regression Chapter 5 Regression Objective: To quantify the linear relationship between an explanatory variable (x) and response variable (y). We can then predict the average response for all subjects
Forecasting in supply chains
1 Forecasting in supply chains Role of demand forecasting Effective transportation system or supply chain design is predicated on the availability of accurate inputs to the modeling process. One of the
Logs Transformation in a Regression Equation
Fall, 2001 1 Logs as the Predictor Logs Transformation in a Regression Equation The interpretation of the slope and intercept in a regression change when the predictor (X) is put on a log scale. In this
Correlation key concepts:
CORRELATION Correlation key concepts: Types of correlation Methods of studying correlation a) Scatter diagram b) Karl pearson s coefficient of correlation c) Spearman s Rank correlation coefficient d)
Time Series AS90641. This Is How You Do. Kim Freeman
AS90641 This Is How You Do Time Series Kim Freeman This book covers NZQA, Level 3 Mathematics Statistics and Modelling 3.1 Determine the Trend for Time Series Level: 3, Credits: 3, Assessment: Internal
Regression and Correlation
Regression and Correlation Topics Covered: Dependent and independent variables. Scatter diagram. Correlation coefficient. Linear Regression line. by Dr.I.Namestnikova 1 Introduction Regression analysis
Module 6: Introduction to Time Series Forecasting
Using Statistical Data to Make Decisions Module 6: Introduction to Time Series Forecasting Titus Awokuse and Tom Ilvento, University of Delaware, College of Agriculture and Natural Resources, Food and
table to see that the probability is 0.8413. (b) What is the probability that x is between 16 and 60? The z-scores for 16 and 60 are: 60 38 = 1.
Review Problems for Exam 3 Math 1040 1 1. Find the probability that a standard normal random variable is less than 2.37. Looking up 2.37 on the normal table, we see that the probability is 0.9911. 2. Find
WEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6
WEB APPENDIX 8A Calculating Beta Coefficients The CAPM is an ex ante model, which means that all of the variables represent before-thefact, expected values. In particular, the beta coefficient used in
Time series Forecasting using Holt-Winters Exponential Smoothing
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
TIME SERIES ANALYSIS AS A MEANS OF MANAGERIA DECISION MAKING IN MANUFACTURING INDUSTRY
TIME SERIES ANALYSIS AS A MEANS OF MANAGERIA DECISION MAKING IN MANUFACTURING INDUSTRY 1 Kuranga L.J, 2 Ishola James.A, and 3 Ibrahim Hamzat G. 1 Department of Statistics Kwara State Polytechnic Ilorin,Nigeria
What are the place values to the left of the decimal point and their associated powers of ten?
The verbal answers to all of the following questions should be memorized before completion of algebra. Answers that are not memorized will hinder your ability to succeed in geometry and algebra. (Everything
Forecasting in STATA: Tools and Tricks
Forecasting in STATA: Tools and Tricks Introduction This manual is intended to be a reference guide for time series forecasting in STATA. It will be updated periodically during the semester, and will be
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Module 7 Test Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. You are given information about a straight line. Use two points to graph the equation.
Review of Fundamental Mathematics
Review of Fundamental Mathematics As explained in the Preface and in Chapter 1 of your textbook, managerial economics applies microeconomic theory to business decision making. The decision-making tools
NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
SEQUENCES ARITHMETIC SEQUENCES. Examples
SEQUENCES ARITHMETIC SEQUENCES An ordered list of numbers such as: 4, 9, 6, 25, 36 is a sequence. Each number in the sequence is a term. Usually variables with subscripts are used to label terms. For example,
Time Series Analysis. 1) smoothing/trend assessment
Time Series Analysis This (not surprisingly) concerns the analysis of data collected over time... weekly values, monthly values, quarterly values, yearly values, etc. Usually the intent is to discern whether
Exercise 1.12 (Pg. 22-23)
Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.
Exponential Smoothing with Trend. As we move toward medium-range forecasts, trend becomes more important.
Exponential Smoothing with Trend As we move toward medium-range forecasts, trend becomes more important. Incorporating a trend component into exponentially smoothed forecasts is called double exponential
What does the number m in y = mx + b measure? To find out, suppose (x 1, y 1 ) and (x 2, y 2 ) are two points on the graph of y = mx + b.
PRIMARY CONTENT MODULE Algebra - Linear Equations & Inequalities T-37/H-37 What does the number m in y = mx + b measure? To find out, suppose (x 1, y 1 ) and (x 2, y 2 ) are two points on the graph of
Manual on Air Traffic Forecasting
Doc 8991 AT/722/3 Manual on Air Traffic Forecasting Approved by the Secretary General and published under his authority Third Edition 2006 International Civil Aviation Organization AMENDMENTS The issue
Objectives of Chapters 7,8
Objectives of Chapters 7,8 Planning Demand and Supply in a SC: (Ch7, 8, 9) Ch7 Describes methodologies that can be used to forecast future demand based on historical data. Ch8 Describes the aggregate planning
CHAPTER 11 FORECASTING AND DEMAND PLANNING
OM CHAPTER 11 FORECASTING AND DEMAND PLANNING DAVID A. COLLIER AND JAMES R. EVANS 1 Chapter 11 Learning Outcomes l e a r n i n g o u t c o m e s LO1 Describe the importance of forecasting to the value
Demand forecasting & Aggregate planning in a Supply chain. Session Speaker Prof.P.S.Satish
Demand forecasting & Aggregate planning in a Supply chain Session Speaker Prof.P.S.Satish 1 Introduction PEMP-EMM2506 Forecasting provides an estimate of future demand Factors that influence demand and
2013 MBA Jump Start Program. Statistics Module Part 3
2013 MBA Jump Start Program Module 1: Statistics Thomas Gilbert Part 3 Statistics Module Part 3 Hypothesis Testing (Inference) Regressions 2 1 Making an Investment Decision A researcher in your firm just
Notes on the SHARP EL-738 calculator
Chapter 1 Notes on the SHARP EL-738 calculator General The SHARP EL-738 calculator is recommended for this module. The advantage of this calculator is that it can do basic calculations, financial calculations
HIBBING COMMUNITY COLLEGE COURSE OUTLINE
HIBBING COMMUNITY COLLEGE COURSE OUTLINE COURSE NUMBER & TITLE: - Beginning Algebra CREDITS: 4 (Lec 4 / Lab 0) PREREQUISITES: MATH 0920: Fundamental Mathematics with a grade of C or better, Placement Exam,
business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
Ch.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 : [email protected] Demand Forecasting. Definition. An estimate
Algebra I Credit Recovery
Algebra I Credit Recovery COURSE DESCRIPTION: The purpose of this course is to allow the student to gain mastery in working with and evaluating mathematical expressions, equations, graphs, and other topics,
I. INTRODUCTION OBJECTIVES OF THE STUDY
ISSN: 2349-7637 (Online) RESEARCH HUB International Multidisciplinary Research Journal (RHIMRJ) Research Paper Available online at: www.rhimrj.com An Analysis of Working Capital Trends P. R. Halani 1st
Algebraic expressions are a combination of numbers and variables. Here are examples of some basic algebraic expressions.
Page 1 of 13 Review of Linear Expressions and Equations Skills involving linear equations can be divided into the following groups: Simplifying algebraic expressions. Linear expressions. Solving linear
HOW TO USE YOUR HP 12 C CALCULATOR
HOW TO USE YOUR HP 12 C CALCULATOR This document is designed to provide you with (1) the basics of how your HP 12C financial calculator operates, and (2) the typical keystrokes that will be required on
2. What is the general linear model to be used to model linear trend? (Write out the model) = + + + or
Simple and Multiple Regression Analysis Example: Explore the relationships among Month, Adv.$ and Sales $: 1. Prepare a scatter plot of these data. The scatter plots for Adv.$ versus Sales, and Month versus
MGT 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
Forecasting Methods. What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes?
Forecasting Methods What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes? Prod - Forecasting Methods Contents. FRAMEWORK OF PLANNING DECISIONS....
Linear Equations. Find the domain and the range of the following set. {(4,5), (7,8), (-1,3), (3,3), (2,-3)}
Linear Equations Domain and Range Domain refers to the set of possible values of the x-component of a point in the form (x,y). Range refers to the set of possible values of the y-component of a point in
CTL.SC1x -Supply Chain & Logistics Fundamentals. Time Series Analysis. MIT Center for Transportation & Logistics
CTL.SC1x -Supply Chain & Logistics Fundamentals Time Series Analysis MIT Center for Transportation & Logistics Demand Sales By Month What do you notice? 2 Demand Sales by Week 3 Demand Sales by Day 4 Demand
AP Physics 1 and 2 Lab Investigations
AP Physics 1 and 2 Lab Investigations Student Guide to Data Analysis New York, NY. College Board, Advanced Placement, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks
Module 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
TIME SERIES ANALYSIS
TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 [email protected]. Introduction Time series (TS) data refers to observations
Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs
PRODUCTION PLANNING AND CONTROL CHAPTER 2: FORECASTING Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs
1.3 Algebraic Expressions
1.3 Algebraic Expressions A polynomial is an expression of the form: a n x n + a n 1 x n 1 +... + a 2 x 2 + a 1 x + a 0 The numbers a 1, a 2,..., a n are called coefficients. Each of the separate parts,
INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT
INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 : FORECASTING TECHNIQUES Dr. Ravi Mahendra Gor Associate Dean ICFAI Business School ICFAI HOuse, Nr. GNFC INFO Tower S. G. Road Bodakdev Ahmedabad-380054
INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT
58 INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT Sudipa Sarker 1 * and Mahbub Hossain 2 1 Department of Industrial and Production Engineering Bangladesh
Vocabulary Words and Definitions for Algebra
Name: Period: Vocabulary Words and s for Algebra Absolute Value Additive Inverse Algebraic Expression Ascending Order Associative Property Axis of Symmetry Base Binomial Coefficient Combine Like Terms
430 Statistics and Financial Mathematics for Business
Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions
Simple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
Problem Solving and Data Analysis
Chapter 20 Problem Solving and Data Analysis The Problem Solving and Data Analysis section of the SAT Math Test assesses your ability to use your math understanding and skills to solve problems set in
Straightening Data in a Scatterplot Selecting a Good Re-Expression Model
Straightening Data in a Scatterplot Selecting a Good Re-Expression What Is All This Stuff? Here s what is included: Page 3: Graphs of the three main patterns of data points that the student is likely to
The Strategic Role of Forecasting in Supply Chain Management and TQM
Forecasting A forecast is a prediction of what will occur in the future. Meteorologists forecast the weather, sportscasters and gamblers predict the winners of football games, and companies attempt to
Tutorial on Using Excel Solver to Analyze Spin-Lattice Relaxation Time Data
Tutorial on Using Excel Solver to Analyze Spin-Lattice Relaxation Time Data In the measurement of the Spin-Lattice Relaxation time T 1, a 180 o pulse is followed after a delay time of t with a 90 o pulse,
(More Practice With Trend Forecasts)
Stats for Strategy HOMEWORK 11 (Topic 11 Part 2) (revised Jan. 2016) DIRECTIONS/SUGGESTIONS You may conveniently write answers to Problems A and B within these directions. Some exercises include special
TIME SERIES ANALYSIS
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi- 110 012 [email protected] 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS
0 The full syllabus 0 The full syllabus continued PAPER C0 FUNDAMENTALS OF BUSINESS MATHEMATICS Syllabus overview This paper primarily deals with the tools and techniques to understand the mathematics
RELEVANT TO ACCA QUALIFICATION PAPER P3. Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam
RELEVANT TO ACCA QUALIFICATION PAPER P3 Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam Business forecasting and strategic planning Quantitative data has always been supplied
Time-Series Forecasting and Index Numbers
CHAPTER 15 Time-Series Forecasting and Index Numbers LEARNING OBJECTIVES This chapter discusses the general use of forecasting in business, several tools that are available for making business forecasts,
Correlation. What Is Correlation? Perfect Correlation. Perfect Correlation. Greg C Elvers
Correlation Greg C Elvers What Is Correlation? Correlation is a descriptive statistic that tells you if two variables are related to each other E.g. Is your related to how much you study? When two variables
PITFALLS 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
Indiana State Core Curriculum Standards updated 2009 Algebra I
Indiana State Core Curriculum Standards updated 2009 Algebra I Strand Description Boardworks High School Algebra presentations Operations With Real Numbers Linear Equations and A1.1 Students simplify and
Causal Forecasting Models
CTL.SC1x -Supply Chain & Logistics Fundamentals Causal Forecasting Models MIT Center for Transportation & Logistics Causal Models Used when demand is correlated with some known and measurable environmental
USING SEASONAL AND CYCLICAL COMPONENTS IN LEAST SQUARES FORECASTING MODELS
Using Seasonal and Cyclical Components in Least Squares Forecasting models USING SEASONAL AND CYCLICAL COMPONENTS IN LEAST SQUARES FORECASTING MODELS Frank G. Landram, West Texas A & M University Amjad
2) The three categories of forecasting models are time series, quantitative, and qualitative. 2)
Exam Name TRUE/FALSE. Write 'T' if the statement is true and 'F' if the statement is false. 1) Regression is always a superior forecasting method to exponential smoothing, so regression should be used
Brunswick High School has reinstated a summer math curriculum for students Algebra 1, Geometry, and Algebra 2 for the 2014-2015 school year.
Brunswick High School has reinstated a summer math curriculum for students Algebra 1, Geometry, and Algebra 2 for the 2014-2015 school year. Goal The goal of the summer math program is to help students
( ) FACTORING. x In this polynomial the only variable in common to all is x.
FACTORING Factoring is similar to breaking up a number into its multiples. For example, 10=5*. The multiples are 5 and. In a polynomial it is the same way, however, the procedure is somewhat more complicated
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-
The Big Picture. Correlation. Scatter Plots. Data
The Big Picture Correlation Bret Hanlon and Bret Larget Department of Statistics Universit of Wisconsin Madison December 6, We have just completed a length series of lectures on ANOVA where we considered
Forecasting DISCUSSION QUESTIONS
4 C H A P T E R Forecasting DISCUSSION QUESTIONS 1. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When
Florida Math for College Readiness
Core Florida Math for College Readiness Florida Math for College Readiness provides a fourth-year math curriculum focused on developing the mastery of skills identified as critical to postsecondary readiness
Algebra 1 If you are okay with that placement then you have no further action to take Algebra 1 Portion of the Math Placement Test
Dear Parents, Based on the results of the High School Placement Test (HSPT), your child should forecast to take Algebra 1 this fall. If you are okay with that placement then you have no further action
Algebra I Vocabulary Cards
Algebra I Vocabulary Cards Table of Contents Expressions and Operations Natural Numbers Whole Numbers Integers Rational Numbers Irrational Numbers Real Numbers Absolute Value Order of Operations Expression
Smoothing methods. Marzena Narodzonek-Karpowska. Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel
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