FORECASTING FLUCTUATIONS OF ASPHALT CEMENT PRICE INDEX IN GEORGIA

Similar documents
BUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7

Suan Sunandha Rajabhat University

TIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA

Forecasting Unemployment Rates in the UK and EU

Comparing the Univariate Modeling Techniques, Box-Jenkins and Artificial Neural Network (ANN) for Measuring of Climate Index

Figure 1. Time Series Plot of arrivals from Western Europe

MODELING INFLATION RATES IN NIGERIA: BOX-JENKINS APPROACH. I. U. Moffat and A. E. David Department of Mathematics & Statistics, University of Uyo, Uyo

STAT 115: Introductory Methods for Time Series Analysis and Forecasting. Concepts and Techniques

Austrian Inflation Rate

FORECASTING. Methods and Applications. Third Edition. Spyros Makridakis. European Institute of Business Administration (INSEAD) Steven C Wheelwright

Foundations - 1. Time-series Analysis, Forecasting. Temporal Information Retrieval

SOLVING PROBLEMS BASED ON WINQSB FORECASTING TECHNIQUES

Forecasting: Methods and Applications

Forecasting of the Austrian Inflation Rate

A stochastic modeling for paddy production in Tamilnadu

Time Series and Forecasting

Prediction of Grain Products in Turkey

Exponential Smoothing. INSR 260, Spring 2009 Bob Stine

FORECASTING COARSE RICE PRICES IN BANGLADESH

Frequency Forecasting using Time Series ARIMA model

22/04/2014. Economic Research

Time Series and Forecasting

Implementation of ARIMA Model for Ghee Production in Tamilnadu

AUTO SALES FORECASTING FOR PRODUCTION PLANNING AT FORD

CHAPTER 18. Time Series Analysis and Forecasting

SCIENCE & TECHNOLOGY

Forecasting Major Vegetable Crops Productions in Tunisia

FinQuiz Notes

Prashant Pant 1, Achal Garg 2 1,2 Engineer, Keppel Offshore and Marine Engineering India Pvt. Ltd, Mumbai. IJRASET 2013: All Rights are Reserved 356

Time Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia

FORECASTING SUGARCANE PRODUCTION IN INDIA WITH ARIMA MODEL

Forecasting. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned

A Comparison of Time Series Models for Forecasting Outbound Air Travel Demand *

MODELLING TIME SERIES WITH CONDITIONAL HETEROSCEDASTICITY

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:

Problem set 1 - Solutions

Chapter 13: Forecasting

DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS

Time-Series Analysis. Dr. Seetha Bandara Dept. of Economics MA_ECON

Automatic Forecasting

Elements of Multivariate Time Series Analysis

Forecasting. Simon Shaw 2005/06 Semester II

Introduction to Forecasting

Lesson 2: Analysis of time series

arxiv: v1 [stat.me] 5 Nov 2008

at least 50 and preferably 100 observations should be available to build a proper model

Econ 300/QAC 201: Quantitative Methods in Economics/Applied Data Analysis. 17th Class 7/1/10

= observed volume on day l for bin j = base volume in jth bin, and = residual error, assumed independent with mean zero.

A Comparison of the Forecast Performance of. Double Seasonal ARIMA and Double Seasonal. ARFIMA Models of Electricity Load Demand

Univariate ARIMA Models

Forecasting using R. Rob J Hyndman. 1.3 Seasonality and trends. Forecasting using R 1

Time Series Analysis -- An Introduction -- AMS 586

Short-Term Load Forecasting Using ARIMA Model For Karnataka State Electrical Load

ADDING EMD PROCESS AND FILTERING ANALYSIS TO ENHANCE PERFORMANCES OF ARIMA MODEL WHEN TIME SERIES IS MEASUREMENT DATA

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY

Introduction to Regression Analysis. Dr. Devlina Chatterjee 11 th August, 2017

Minitab Project Report - Assignment 6

Modified Holt s Linear Trend Method

ARIMA modeling to forecast area and production of rice in West Bengal

Reconnect 2017 Module: Time Series Applications in Energy-Related Issues Instructor Version

Empirical Approach to Modelling and Forecasting Inflation in Ghana

The data was collected from the website and then converted to a time-series indexed from 1 to 86.

Forecasting the Prices of Indian Natural Rubber using ARIMA Model

Forecasting using R. Rob J Hyndman. 2.4 Non-seasonal ARIMA models. Forecasting using R 1

Transformations for variance stabilization

Time Series Analysis of Stock Prices Using the Box- Jenkins Approach

Evaluation of Some Techniques for Forecasting of Electricity Demand in Sri Lanka

Multivariate forecasting with VAR models

Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University

Research Article A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

Robust control charts for time series data

Forecasting Using Time Series Models

Forecasting. Copyright 2015 Pearson Education, Inc.

Technical note on seasonal adjustment for Capital goods imports

Lecture 7: Exponential Smoothing Methods Please read Chapter 4 and Chapter 2 of MWH Book

Time Series Analysis

A MACRO-DRIVEN FORECASTING SYSTEM FOR EVALUATING FORECAST MODEL PERFORMANCE

Design of a Weather-Normalization Forecasting Model

Application of Quantitative Forecasting Models in a Manufacturing Industry

Based on the original slides from Levine, et. all, First Edition, Prentice Hall, Inc

Stochastic Processes

Multivariate Regression Model Results

TIME SERIES DATA PREDICTION OF NATURAL GAS CONSUMPTION USING ARIMA MODEL

Short-term electricity demand forecasting in the time domain and in the frequency domain

Lab: Box-Jenkins Methodology - US Wholesale Price Indicator

Asitha Kodippili. Deepthika Senaratne. Department of Mathematics and Computer Science,Fayetteville State University, USA.

Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University

Decision 411: Class 9. HW#3 issues

TIMES SERIES INTRODUCTION INTRODUCTION. Page 1. A time series is a set of observations made sequentially through time

176 Index. G Gradient, 4, 17, 22, 24, 42, 44, 45, 51, 52, 55, 56

Study of Time Series and Development of System Identification Model for Agarwada Raingauge Station

Lecture 1: Introduction to Forecasting

Statistical Models for Projecting the Situation

Prediction of Seasonal Rainfall Data in India using Fuzzy Stochastic Modelling

ISSN Original Article Statistical Models for Forecasting Road Accident Injuries in Ghana.

TRANSFER FUNCTION MODEL FOR GLOSS PREDICTION OF COATED ALUMINUM USING THE ARIMA PROCEDURE

INTRODUCTION TO TIME SERIES ANALYSIS. The Simple Moving Average Model

Forecasting Precipitation Using SARIMA Model: A Case Study of. Mt. Kenya Region

Time Series Analysis of Currency in Circulation in Nigeria

A FUZZY TIME SERIES-MARKOV CHAIN MODEL WITH AN APPLICATION TO FORECAST THE EXCHANGE RATE BETWEEN THE TAIWAN AND US DOLLAR.

Transcription:

FORECASTING FLUCTUATIONS OF ASPHALT CEMENT PRICE INDEX IN GEORGIA Mohammad Ilbeigi, Baabak Ashuri, Ph.D., and Yang Hui Economics of the Sustainable Built Environment (ESBE) Lab, School of Building Construction Georgia Institute of Technology

Overview Introduction - Asphalt cement price index - Problems related to asphalt cement price variation Research Objective Research Background Research Approach - Univariate time series forecasting models Summary of Results - In sample fitting models - Out of sample forecasting Conclusion Limitations and Future Works

Introduction

Asphalt Cement Index ($/ton) 100 200 300 400 500 600 700 Introduction Asphalt Cement Price Index in Georgia from 1995 to 2012 2000 2005 2010 Time

Research Motivation Overall Problem Significant volatility in the cost of Asphalt Cement leads to uncertainty about transportation project cost Problems for Owners Price Volatility Uncertain Estimation Problem for Contractors

Research Motivation Related Issues to Owner Organizations Hidden price contingencies Very short-term price guarantees Not enough bidders Related Issues to Contractors Bid loss due to cost overestimation Profit loss due to cost underestimation

Research Objective Objective Create appropriate univariate time series models for estimating and forecasting fluctuations in asphalt cement price index.

Research Background Forecasting Models Causal Methods Pattern Analysis Methods Predicted variable is determined by independent explanatory variables Predicted variable is determined by its historical behaviors Regression Models Time Series Models

Research Approach: Time Series Models Time Series Data Set Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting A time series is a set of data points which are recorded at uniform time intervals. In this research, our time series data set consists of monthly asphalt cement price index in the state of Georgia from Sep 1995 to June 2012 GDOT determines the index based on the average of prices from around 20 different suppliers after removing the minimum and maximum prices.

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Time series analysis methods are used to extract meaningful characteristics of a time series data set Out of Sample Forecasting

Time Series Forecasting Process Time Series Data Set Time Series Analysis Stationary: A time series is stationary if its statistical properties do not depend on time. In-Sample Model Fitting Out of Sample Forecasting

ACF -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Time Series Forecasting Process Time Series Data Set Time Series Analysis Stationary: Series AC_monthly_price Auto Correlation Function Plot: In-Sample Model Fitting Out of Sample Forecasting 0 20 40 60 80 100 Lag

Time Series Forecasting Process Time Series Data Set Time Series Analysis Seasonality: Seasonality displays certain cyclical or periodic behaviors during the time. In-Sample Model Fitting Out of Sample Forecasting

ACF -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Time Series Forecasting Process Time Series Data Set Seasonality: Series diff(ac_monthly_price) First Difference Auto Correlation Function Plot: Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting 0 20 40 60 80 100 Lag

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-sample model fitting uses historical data set to estimate parameters of the model and fit the model with actual data. In-Sample Model Fitting Out of Sample Forecasting

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting Sep 1995 June 2011 June 2011 In Sample Period: Sep 1995 to June 2011 Out of Sample Period: July 2011 to June 2012

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting Models: Simple Moving Average (SMA) Holt Exponential Smoothing Holt-Winters Exponential Smoothing ARIMA Seasonal ARIMA

Time Series Models Modeling Assumptions: Time Series Methodologies Simple Moving-Average (SMA) Holt Exponential Smoothing (Holt ES) Holt-Winters Exponential Smoothing (Holt-Winters ES) Auto-Regressive Integrated Moving- Average (ARIMA) Seasonal ARIMA Modeling Assumptions N.A. Underlying data show trends Underlying data show trends & seasonality Underlying data are nonstationary Model residuals are white noise Underlying data are nonstationary & seasonal Model residuals are white noise

Time Series Models Modeling Parameters: Time Series Methodologies Modeling Parameters Simple Moving-Average (SMA) N.A. Holt Exponential Smoothing (Holt ES) α β Holt-Winters Exponential Smoothing (Holt-Winters ES) α β γ Auto-Regressive Integrated Moving-Average (ARIMA) p d q Seasonal ARIMA p d q P D Q

Asphalt Cement Index ($/ton) 100 200 300 400 500 600 700 Time Series Forecasting Process Time Series Data Set Time Series Analysis HoltWinterES model and forecast values real value fitted value forecasted value In-Sample Model Fitting Out of Sample Forecasting 2000 2005 2010 Time

Time Series Forecasting Process Time Series Data Set Time Series Analysis Error Measures: Mean Absolute Percentage Error (MAPE) In-Sample Model Fitting Out of Sample Forecasting 1 Ŷ(t)-Y(t) MAPE= 100% N Y(t) N t=1

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Error Measures: Mean Absolute Percentage Error (MAPE) Mean Square Error (MSE) Out of Sample Forecasting 1 Ŷ(t)-Y(t) MAPE= 100% N 1 MSE= ˆ N N t=1 Y(t) N 2 Y(t)-Y(t) t=1

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting Error Measures: Mean Absolute Percentage Error (MAPE) Mean Square Error (MSE) Mean Absolute Error (MAE) 1 Ŷ(t)-Y(t) MAPE= 100% N 1 MSE= ˆ N N t=1 Y(t) N 2 Y(t)-Y(t) t=1 1 MAE= Y(t)-Y(t) ˆ N N t=1

Time Series Forecasting Process Time Series Data Set In Sample Model Fitting Error: Time Series Analysis In-Sample Model Fitting SMA ARIMA Seasonal ARIMA Holt ES Holt Winters ES MAPE 6.97% 6.91% 7.06% 8.24% 10.53% MSE 744.91 671.15 615.75 850.38 1080.70 MAE 16.19 14.84 14.78 17.19 21.39 Out of Sample Forecasting

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Out-of-sample forecasting attempts to forecast future values of a variable by using the time series models and their parameters that were determined via in-sample model fitting based on the historical data. Out of Sample Forecasting

Time Series Forecasting Process Time Series Data Set Time Series Analysis In-Sample Model Fitting Out of Sample Forecasting Models: Simple Moving Average (SMA) Holt Exponential Smoothing Holt-Winters Exponential Smoothing ARIMA Seasonal ARIMA

Asphalt Cement Index ($/ton) 100 200 300 400 500 600 700 Time Series Forecasting Process Time Series Data Set Time Series Analysis HoltWinterES model and forecast values real value fitted value forecasted value In-Sample Model Fitting Out of Sample Forecasting 2000 2005 2010 Time

Time Series Forecasting Process Time Series Data Set Forecasting Error Time Series Analysis In-Sample Model Fitting Error Measures SMA ARIMA Seasonal ARIMA Holt ES Holt Winters ES MAPE 4.73% 6.52% 10.03% 35.15% 5.3% MSE 1091.75 2029.44 5845.04 51364.11 1157.18 MAE 28.90 37.97 65.65 255.75 34.47 Out of Sample Forecasting

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 RealValue 650 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 RealValue 650 SMA 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 RealValue 650 Arima 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 RealValue 650 Seasonal Arima 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 RealValue 650 HoltES 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 RealValue 650 Holtwinter ES 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Asphalt Cement Price Index ($/ton) Results: Forecasted Values 950 850 750 650 RealValue SMA Arima Seasonal Arima HoltES Holtwinter ES 550 450 1 2 3 4 5 6 7 8 9 10 11 12 Time

Price 300 400 500 600 700 800 Results: Forecasted Confidence Intervals Forecasts from ARIMA(2,1,1) Confidence Intervals: forecasted values 80%pred interval 95%pred interval June 11 Dec 11 Feb 12 Apr 12 June 12 Time

Conclusion Accurate forecasting of asphalt cement price index is possible by Time Series models Accurate forecasting of material price can help: Contractors to submit more accurate and competitive bids State DOTs to consider more accurate budget Contractors and State DOTs to measure their risks and develop appropriate risk management strategies State DOTs to examine financial implications of offering Price Adjustment Clause (PAC) for asphalt cement

Limitations and Future Works Limitations: 1- Not appropriate for long term forecasting 2- Unable to perform well when a discrete jump occurs Measuring the value of Price Adjustment Clause (PAC) Develop procedure to determine risk contingencies Multivariate time series forecasting models

Acknowledgment Georgia Tech University Transportation Center Georgia Department of Transportation (GDOT) Dr. Peter Wu Ms. Georgene Geary Ms. Supriya Kamatkar Mr. David Jared

Thank you for your attention!

2024 © sciencedocbox.com Privacy Policy | Terms of Service | Feedback