...CHAPTER 1 INTRODUCTION 1.1 PRACTICUM INTRODUCTION According to Macmillan English Dictionary for Advanced Learners Second Edition (2011), practicum or practical means involving or relating to real situations and events. In the other words, it is the activity which not only about theory but facing the real world works. Students will be trained to apply the theory during this period. In Universiti Utara Malaysia (UUM), practicum is obligated for students in some courses as a fulfillment in the course structure. Practicum program is also designed to provide and expose student to real world problem situations before the end of graduation. From this program, students are given the opportunity to practice and apply their understanding and theoretical knowledge that have been learned in classroom with the help and guidance from the university and the attached organization. It is a platform of training and preparing the student before entering the job market. Practicum program is compulsory for all students pursuing Bachelor of Business Mathematics with Honors in UUM. This program can also be seen as a method to integrate the theoretical knowledge with the hands-on training. In this way, students would have an opportunity to apply their understanding of theories into the real working environment within industries. Practicum program provides opportunities for students to improve two vital skills which are soft kills and hard skills. It aims to help student to apply these two skills...
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...predict future return, volatility and VaR using the model we built for the last sub-period based on rolling forecast procedure. Though the fitted values of 10th sub-period model are very acceptable, the predicted values are reasonable yet far from satisfactory. Only some future volatility can be predicted using one-step ahead rolling forecast, and return prediction is not much better than just using historical mean, which is almost 0, to predict. These results suggest that external variables are needed for more accurate predictions, time series models alone are not sufficient. Data S&P500 index daily closing price from 1993 to 2012 are obtained from yahoo finance website. It is one of the best measures of current state of U.S. domestic economy, therefore by studying its fluctuations, consistency, seasonality and make predictions, one can determine if it is a good time to invest in U.S. stock market. Methodology We first examined the time series plots, ACF plots and PACF plots of whole period and each sub-period. Then ARIMA models for log-returns mean part and GARCH models for the conditional volatility of the log-returns are fitted. After selecting our final models for each subperiod, the final models were compared in order to check for model consistency and potential market seasonality. Finally the...
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...DEVELOPMENT OF TIME SERIES MODEL TO STUDY HISTORICAL TRE ND OF ROAD TRAFFIC ACCIDENTS IN THE UNI TED STATES AND INSPECT THE FACTORS AFFECTING THE TREND Ashutosh Kedia M.Tech Project Thesis 2015 DEVELOPMENT OF TIME SERIES MODEL TO STUDY THE HISTORICAL TREND OF ROAD TRAFFIC ACCIDENTS IN THE UNITED STATES AND INSPECT THE FACT ORS AFFECTING THE TREND Thesis submitted to the Indian Institute of Technology, Kharagpur For award of the degree of Master of Technology by Ashutosh Kedia Under the guidance of Prof. Sudeshna Mitra DEPARTMENT OF CIVIL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR MAY 2015 ©2015 Ashutosh Kedia. All rights reserved. Page i M.Tech Project Thesis 2015 APPROVAL OF THE VIVA-VOCE BOARD 05/05/15 Certified that the thesis entitled DEVELOPMENT OF TIME SERIES MODEL TO STUDY HISTORICAL TREND OF ROAD TRAFFIC ACCIDENTS IN THE UNITED STATES AND INSPECT THE FACTORS AFFECTING THE TREND submitted by ASHUTOSH KEDIA to the Indian Institute of Technology, for the award of the degree, Master of Technology, has been accepted by the external examiners and that the student has successfully defended the thesis in the viva-voce examination held today. (External Examiner) (Chairman) Page ii (Supervisor) M.Tech Project Thesis 2015 CERTIFICATE This is to certify that the thesis entitled “Development of Time Series Model to Study Historical Trend of Road Traffic Accidents in the United States and Inspect the Factors Affecting the Trend” submitted by Ashutosh Kedia...
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...Published in Graham J. Hooley and Michael K. Hussey (Eds.), Quantitative Methods in Marketing, Second Edition. London: International Thompson Business Press, 1999, pp. 92-119. Forecasting for Marketing J. Scott Armstrong The Wharton School, University of Pennsylvania Roderick J. Brodie Department of Marketing, University of Auckland Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios. INTRODUCTION Forecasting has long been important to marketing practitioners. For example, Dalrymple (1987), in his survey of 134 U.S. companies, found that 99 percent prepared formal forecasts when they used formal marketing plans. In Dalrymple (1975)...
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...Andrew R. Cohen1, Christopher Bjornsson1, Ying Chen1, Gary Banker2, Ena Ladi3, Ellen Robey3, Sally Temple4, and Badrinath Roysam1 1 Rensselaer Polytechnic Institute, Troy, NY 12180, USA, 2 Oregon Health & Science University, 3181 SW Sam Jackson Park Road, L606, Portland, OR 97239, USA 3 University of California, Berkeley, Berkeley, CA 94720, USA 4 Center for Neuropharmacology & Neuroscience, Albany Medical College, Albany, NY 12208, USA ABSTRACT An algorithmic information theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG), whose connected subgraphs are compared using an adaptive information distance measure, aided by a closed-form multi-dimensional quantization. The summary is the clustering result and feature subset that maximize the gap statistic. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. When applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain. When analyzing intra-cellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification. Finally, it was able to differentiate wild type from genetically modified thymocyte cells. Index Terms: Algorithmic information...
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...the developing economies including Rwanda would appear to have exacerbated fluctuations in exchange rates, developing economies are special examples of high exchange rate, The impact of exchange rate levels on trade has been much debated but the large body of existing empirical literature does not suggest an indubitable comprehensive image of the trade impacts of exchange rate volatility in Rwanda. The review of the theoretical literature on this issue indicates that there is no clear-cut relationship between exchange rate volatility and balance of trade. This study examines the effect of exchange rate volatility and balance of trade sector in Rwanda The analysis followed the empirical methods (econometrics and time series analysis). The researchers used UBJ time series analysis to accomplish all stages (stationarity, identification, estimation, diagnostic checking and forecasting) of the models and models validation was of good quality and can be used in forecasting for future values. Polynomial regression model helped to establish the effects of exchange rate on balance of trade. The results revealed a positive quadratic relationship between exchange rate and balance of trade components and by polynomial regression model estimation, exports and imports will increase as exchange rate increases. The researchers recommended improving strategies and techniques for maintaining the quality of Rwandan exports, decreasing the...
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...CHAPTER = 11 Q-1 | | |Factor |Factor Score for Each Location | | |Location Factor |Weight |A |B |C |D | | |1. Labor climate | 5 |5 | | |Location Factor |Weight |A |B |C |D | | |1. Rent |25 |3 |75 |1 |25 |2 |50 |5 |125 | | |2. Quality of life |20 |2 |40 |5 |100 |5 |100 |4 |80 | | |3. Schools |5 |3 |15 |5 |25 |3 |15 |1 |5 | | |4. Proximity to work |10 |5 |50 |3 |30 |4 |40 |3 |30 | | |5. Proximity to recreation |15 |4 |60 |4 |60 |5 |75 |2 |30 | | |6. Neighborhood security |15 |2 |30 |4 |60 |4 |60 |4 |60 | | |7. Utilities |10 |4 |40 |2 |20 |3 |30 |5 |50 | | |Total ...
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...Eviews Functions Programming in Eviews 7 8 10 18 22 2 2.1 2.2 2.3 Regression Model Introduction Linear Regression Model Nonlinear Regression 34 34 34 52 3 3.1 3.2 3.3 Univariate Time Series: Linear Models Introduction Stationarity and Autocorrelations ARMA processes 54 54 54 59 www.sylvania.com We do not reinvent the wheel we reinvent light. Fascinating lighting offers an infinite spectrum of possibilities: Innovative technologies and new markets provide both opportunities and challenges. An environment in which your expertise is in high demand. Enjoy the supportive working atmosphere within our global group and benefit from international career paths. Implement sustainable ideas in close cooperation with other specialists and contribute to influencing our future. Come and join us in reinventing light every day. Light is OSRAM Download free eBooks at bookboon.com 4 Click on the ad to read more Contents Financial Econometrics 4 4.1 4.2 4.3 4.4 Stationarity and Unit Roots Tests Introduction Unit Roots tests Stationarity tests Example: Purchasing Power Parity 69 69 69 74 75 5 5.1 5.2 5.3 5.4 5.5 Univariate Time Series: Volatility Models Introduction The ARCH Model The GARCH Model GARCH model estimation GARCH Model Extensions 80...
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...cash requirements, and financing needs. A variety of forecasting methods are available. However, consideration has to be given to cost, preparation time, accuracy, and time period. The manager must understand clearly the assumptions on which a particular forecast method is based to obtain maximum benefit. 1 Types of Forecasts Short Term Short-range forecasts typically encompass the immediate future and concern the day to day operations of a firm. A short-term forecast usually only covers a period of a few months and can be considered an “operating” forecast. Medium Term Medium-range forecasts typically span a few months up to a year. A forecast of this length can be considered “tactical” in nature. Long Term Long-range forecasts typically encompass a period longer than 1 to 2 years. These forecasts are considered strategic and are generally related to management’s attempt to plan new products or build new facilities. 2 Forecasting Methods Time Series Regression Qualitative 3 Times Series Time Series Forecasting is the use of a model to predict future values based on previously observed values. They use historical data to predict future behavior. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the...
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...cash requirements, and financing needs. A variety of forecasting methods are available. However, consideration has to be given to cost, preparation time, accuracy, and time period. The manager must understand clearly the assumptions on which a particular forecast method is based to obtain maximum benefit. 1 Types of Forecasts Short Term Short-range forecasts typically encompass the immediate future and concern the day to day operations of a firm. A short-term forecast usually only covers a period of a few months and can be considered an “operating” forecast. Medium Term Medium-range forecasts typically span a few months up to a year. A forecast of this length can be considered “tactical” in nature. Long Term Long-range forecasts typically encompass a period longer than 1 to 2 years. These forecasts are considered strategic and are generally related to management’s attempt to plan new products or build new facilities. 2 Forecasting Methods Time Series Regression Qualitative 3 Times Series Time Series Forecasting is the use of a model to predict future values based on previously observed values. They use historical data to predict future behavior. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the...
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...Choose one of the forecasting methods and explain the rationale behind using it in real-life. Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. Time series forecasting is a category of statistical techniques that uses historical data to predict future behavior. Time series methods assume that what has occurred in the past will continue to occur in the future. Time series methods tend to be most useful for short range forecasting, although they can be used for longer range forecasting. A domestic fast food chain with plans of expanding to China could use the Qualitative method. The Qualitative method uses judgment, expertise, and opinion to make forecasts. They could use surveys and other research techniques to determine if there product is something that customers will want to purchase. By using surveys, you could find out if your product is something, someone would want or need. You could research your competition to find out if people want and are willing to purchase your food. What is the difference between a causal model and a time-series model? Give an example of when each would be used. The time series model forecasts statistical techniques that use historical data. This method is often used in restaurants and retail. The causal method uses mathematical correlation between the forecasted items and factors affecting how the forecasted items behaves. What are some of the problems and drawbacks...
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...based on the information provided by the consumers in the survey and the economic activities of the business can be planned based on the results from survey for the companies expenditure on its expansion or other production plans. In case of opinion polling, it helps to determine potential buyers and their requirements and this can help firms to forecast its product demands and improvement of options that will have to be added for customers to buy the goods. Ch6 DQ.3(a). What are time-series data? What are the possible sources of variation in time-series data? Time-series data is the values of a variable arranged chronologically by days, weeks, months, quarters or years. The time-series analysis attempts to forecast future values of the time series by examining the past observations of the data only. The possible sources of variations are secular trend, cyclical fluctuations, seasonal variations and irregular influences. The trends variation refers to long-run increase or decrease in the data series. Cyclical fluctuations variation are the major...
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...Part 1: Expected future seminar participants for the next four seasons HR Inc. is considering expansion and intends to base its decision on analysis of historical trend in seminar participant numbers. First and foremost, we shall look at the participants’ data for the previous periods, and identify the general trend in terms of any increases or decreases in number of participants over time. We can use quantitative forecasting methods to forecast the future expected class sizes, since we have past data on class sizes. We shall also assume that the past pattern will continue in the future and therefore use the time series forecasting method. Year | Quarter | Time period | Pax | Moving Total | Moving Average | Seasonal Index (Actual/ MA) | 2003 | Jan - Mar | 1 | 35 | | | | | Apr - Jun | 2 | 44 | | | | | Jul - Sep | 3 | 54 | | | | | Oct - Dec | 4 | 49 | | | | 2004 | Jan - Mar | 5 | 68 | 182 | 45.50 | 1.49 | | Apr - Jun | 6 | 61 | 215 | 53.75 | 1.13 | | Jul - Sep | 7 | 61 | 232 | 58.00 | 1.05 | | Oct - Dec | 8 | 75 | 239 | 59.75 | 1.26 | 2005 | Jan - Mar | 9 | 70 | 265 | 66.25 | 1.06 | | Apr - Jun | 10 | 62 | 267 | 66.75 | 0.93 | | Jul - Sep | 11 | 70 | 268 | 67.00 | 1.04 | | Oct - Dec | 12 | 74 | 277 | 69.25 | 1.07 | 2006 | Jan - Mar | 13 | 64 | 276 | 69.00 | 0.93 | | Apr - Jun | 14 | 72 | 270 | 67.50 | 1.07 | | Jul - Sep | 15 | 76 | 280 | 70.00 | 1.09 | | Oct - Dec | 16 | 72 | 286 | 71.50 | 1.01 | 2007 | Jan - Mar...
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...ECO 550 Week 3 Discussion Questions Week 3 DQ 1 "Managing in the Global Economy" Please respond to the following: * *Answer the following discussions based on the Katrina’s Candies scenario: * From the scenario for Katrina’s Candies, assuming the absence of quantitative data, determine the qualitative forecasting techniques that could be used within this scenario. * Now, assume you have acquired some time series data that would enable you to make short, medium, and long term forecasts. Ascertain the quantitative technique that will provide you with the most accurate forecast. Provide a rationale for your responses. Answer: From the scenario for Katrina’s Candies, assuming the absence of quantitative data, determine the qualitative forecasting techniques that could be used within this scenario. Quantitative data are anything that can be expressed as a number, or quantified. Examples of quantitative data are scores on achievement tests, number of hours of study, or weight of a subject. These data may be represented by ordinal, interval or ratio scales and lend themselves to most statistical manipulation. Qualitative data cannot be expressed as a number. Data that represent nominal scales such as gender, socieo economic status, and religious preference are usually considered to be qualitative data. Both types of data are valid types of measurement, and both are used in education journals. Only quantitative data can be analyzed statistically, and thus...
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...Suthikshn Kumar Dept of Information Science, PESIT Bangalore suthikshn.kumar@pes.edu Ishaan Raghunandan Dept of Information Science, PESIT Bangalore ishaan.raghunandan@gmail.com Abstract: In modern greenhouses, several measurement points are required to trace down the local climate parameters in different parts of the enclosure efficiently.A Wireless Sensor Network (WSN) consisting of small-size wireless sensor nodes an attractive and cost-efficient option to build the required measurement system. The most important climatic conditions are the temperature and the light intensity within the greenhouse.This paper describes the implementation and configuration of the wireless sensor network using the Sun SPOT platform.It also uses a Time Series Algorithm to predict the temperature variation. Keywords: WSN,SunSPOT,TimeSeries,Java,Zigbee,Back Propagation 1.Introduction The most important factors for the quality and productivity of plant growth are temperature and light. Continuous monitoring of these environmental variables gives information to the agriculturalist to better understand, how each factor affects growth and how to manage maximal crop productiveness. The optimal greenhouse climate adjustment can enable us to improve productivity and to achieve remarkable energy savings - especially during the winter in northern countries. Increased number of measurement points should not dramatically increase the automation system cost. It should also be possible to easily change the...
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