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Words 1074

Pages 5

QRB/501 Quantitative Reasoning for Business

February 7, 2012

Forecasting with Time Series For most companies, forecasting is very important. Their future can be determined with forecasting and this also helps pin point the problems of the past. Forecasting can be done in many methods, depending on what exactly is being forecasted. A forecasting tool used to determine demand for various commodities or goods in a given marketplace over the course of a typical year (or a shorter time period). Such an index is based on data from previous years that highlights seasonal differences in consumption. In some industries, the seasonality index experiences huge swings. (Business Dictionary, 2012) This forecasting tool is known as seasonal indexing. Find the seasonal index for summer historical inventory data below. Summer Historical Inventory Data Summer historical inventory data measures monthly figures in units four times a year. Data forecasted helps the company with inventory demands for the following year. Organizations should average a certain amount each month for a four year forecast. Organizations estimate demand for that month for the fifth year. Companies should average demands each year, and display average demands per month for that year. Data helps provide the organization, an overview with demands from one year to another year (brainmass, 2004, 2011). Organizations should identify the sequence of observation, forecasting future value, and time series variables. Patterns organize, interpret, and integrate data compiled. Helps the organization to identify, and predict future events taking place (census, 2012).

Importance of Percentages

The percentages will be important in determining the health of the organization, and whether or not the organization is performing above…...

...Time Series Analysis Summary Tokelo Khalema 2008060978 BSc. Actuarial Science University of the Free State Bloemfontein November 1, 2012 Time Series Analysis A time-series is a stochastic process {Xt : t = 1, . . . , T } with a continous state space and discrete time domain. It arises naturally as an ordered series of values observed over time. Examples include daily closing prices of a stock index recorded over several years, say, the ﬂow rate of the River Nile, road casualties in Great Britain over the years 1969-84, etc. Stationary time-series are particularly easy to analyse. A series is stationary if its mean and variance are constant over time. Special aids are available to help determine whether or not a series is stationary. Particularly notable in this regard are the autocorrelation function (ACF) and the partial autocorrelation function (PACF). These are plots of the sample autocorrelation and partial autocorrelation coeﬃcients at various time lags, respectively. If the ACF decays gradually to zero, then the series is non-stationary. If on the other hand the ACF and PACF decay rapidly to zero, then the series is stationary. A series being non-stationary can be brought about by, among others, a trend, irregular ﬂuctuations, or seasonal variation. Non-constant variance, or as commonly called, heteroscedasticity can be eliminated by using a variance-stabilising transformation. A number of ways exist that eliminate a trend. Two of which are, to subtract a regression......

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...ON TIME SERIES ANALYSIS PLANNING AND CONTROL OF OPERATONS FAWAZ MOHAMED KUTTY 215112035 MBA Ist YEAR TIME SERIES ANALYSIS A time series may be defined as a set of values of a variable collected and recorded in a chronological order of the time intervals. Time series is used by statisticians to describe the flow of economic activity. In short time series refers to the data depending on time. It refers to a set of observations concerning any activity against different periods of time. The duration of the time period may be hourly, daily, weekly, monthly or yearly. According to Morris Hamburg “A time series is a set of statistical observations arranged in chronological order”. Therefore time series is also called historical data or historical series. The study of movement of quantitative data through time is referred to as ‘time series analysis’. Time series is of great importance to the planners of economic development and economists. The success of planning depends upon making accurate forecasts of future conditions of economy. It enables the economists to foresee what is likely to come and to analyze the repercussions of past behavior. The analysis of time series enables us to understand the past behavior or performance. Time series analysis can be used to know how the data changes over time and find out probable reasons for such change. UTILITY OF TIME SERIES ANALYSIS Analysis of time series is of relevance whenever a variable is found to vary over time.......

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...Time Series Analysis Yt=observed value of the time series in time period t TRt=the trend component or factorin time period t SNt=the seasonal componentor factorin time period t CLt=the cyclical componentor factorin time period t IRt=the irregular componentor factorin time period t 7.1) CL*IRCL=IR a) SN1=1.191 TR1=240.5 CL1=null IRt=null SN2=1.521 TR2=260.4 CL2=0.998 IR2=0.990 SN3=0.804 TR3=280.4 CL3=0.994 IR3=0.986 SN4=0.484 TR4=300.3 CL4=1.003 IR4=1.008 b) It presents a multiplicative decomposition model Yt=TRt*SNt*CLt*IRt SNt*IRt=YtTRt CLt snt*irt=YtCMAt =YtCMAt Equation of the estimated trend: TRt=Bo+B1t dt=B0+B1t+εt TRt=220.53+19.94(t) c) Yt=trt*snt Y17=220.53+19.9417*1.191=666.6 Y18=220.53+19.9418*1.521=881.6 Y19=220.53+19.9419*0.804=482.1 Y20=220.53+19.9420*0.484=299.9 d) Yt=trt*snt*cl We cannot see a definitive cycle and because the values of cl are close to 1. We do not take it into account. Y21=220.53+19.9421*0.191=761.6 e) Since there are just four years of data and most values are near 1 we cannot discern a well-defined cycle. f) Y21=220.53+19.9421*0.191=761.6 It agrees with the values computed in part c g) Excel Spreadsheet h) Prediction intervals for the next 4 quarters t=17,18,19,20 t=17:654.094,679.542 t=18:869.038,894.542 t=19:469.107,494.556 t=20:286.977,312.426 8.1) Smoothing equation l0=t=1nYtn Which is the average of the first series values lT=αyT+(1-α)lT-1 α:smoothing......

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...measurement of financial development comprises two different groups. The first group of studies measure financial development as a result of the observed outcomes of financial development. These studies include size, access and depth of financial systems as a measure of financial development. The second group includes proxies of a country's legal, business, and political conditions as well as the stability of financial. This group actually measures financial development on the basis of the characteristics of its institutional business and political environment. In this paper it is attempted to measure financial development on the basis of observed outcomes of financial development because the measures adopted by the second group are highly time invariant. The characteristics and observed outcomes of financial development are discussed below. Institutional Environment The Institutional environment of a developed financial system involves policies, regulations, laws, and supervision. Herger et al (2007) found that dysfunctional institutions are one of the main hurdles in financial development. Countries with strong institutional environment and investor's safeguard achieve high levels of financial development (La Porta et al, 1997). The constant monitoring of the financial system with certified international audits is recommended to achieve high levels of financial development. Barth et al (2007) suggested that banks should be rated on international standards, and by......

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...REPORT ON TIME SERIES ANALYSIS REPORT ON TIME SERIES ANALYSIS SUBMITTED TO M. KHAIRUL HOSSAIN PROFESSOR Department Of Finance University Of Dhaka SUBMITTED BY Group – 17 Section-A BBA 12th Batch Department Of Finance WE ARE... |Sl. No |Name |Roll No | |1. |Dulal Paul |12-143 | |2. |Rahat Hussain Md. Zaidy |12-149 | |3. |MD. Arif Hasan |12-150 | |4. |MD. Khurshid Alam |12-170 | |5. |MD. Saiful Islam |12-254 | Letter of Transmittal Date: 16th September, 2008 M. Khairul Hossain Professor Department Of Finance Faculty of Business Studies University of Dhaka Subject: Submission of report We are pleased to submit the following report on the “Time Series Analysis”. By completing the report, we have got acquainted importance and relevance of time series on business application. We also perceived idea on the whole process of Time Series Analysis. We acquired knowledge about the......

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...potential signaling slowing (accelerating) job growth. On a week-to-week basis, claims are quite volatile, and many analysts therefore track a four week moving average to get a better sense of the underlying trend. It typically takes a sustained move of at least 30K in claims to signal a meaningful change in job growth. Goal: The goal of this project is to model seasonally adjusted initial claims and then predict the initial claims for the future because it is used by financial institutions, especially in currency trading, to get a better idea about the direction of the United States economy. Note: The data is acquired from the Federal Reserve Economic Data website: http://research.stlouisfed.org/fred2/series/ICSA II. Exploratory Data Analysis The plot of the time series of the weekly initial claims looks stationary but contains a lot of outliers in the data. Also, the ACF plot and the PACF plots are shown, and it is obvious that the data does not follow a white noise process. Because no clear order can be seen from these plots, I decided to take the first difference of the data. Now, looking at the first difference of the weekly initial unemployment claims, the data seems to be a little smoother but still contains some outliers. Now, there seems to be a more discernible order in the data, and once again, it does not follow a white noise process as seen by the ACF and PACF plots. I put these plots again just so that the plots can be a little bit more......

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...importantly, it is essential to understand how it affects us monetarily. When we can understand trends and forecast the pricing of natural gas, we can improve our financial planning, resource allocation, etc. II. Data Set Description and Methods Used This paper will be conducting a time series analysis on U.S. Natural Gas prices from January 2002 through December 2012. Prices were collected by the Energy Information Administration on a monthly basis, and the prices are measured in dollars per thousand cubic feet. The raw data set is much more extensive, and measures data such as wellhead price, import and export price, etc. I chose to neglect this in my data set for simplicity sake and because it is more applicable to look at prices directly affecting everyday individual consumers. The methods used in the time series analysis of U.S. Natural Gas Prices include non-seasonal differencing, exploring autocorrelation and partial autocorrelation functions, ARIMA modeling, conducting diagnostics on modeling by looking at residual normality [QQ plot, Shapiro Test], Box-Pierce test for autocorrelation, and other methods as well. III. Detailed Results (Please see below) This is a time series plot of U.S. Natural Gas Prices from January 2002 – December 2012. As we can see, there is an apparent trend and seasonality present that we need to identify and eliminate before we develop an ARIMA model and forecast. Next, we will look at the ACF and PACF of the......

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...of carbon dioxide into the air such as cement production and the purification of raw metals. Subsequently, more efforts may have been put into the progress of non polluting activities and also a better implementation of environmental policies in the United Kingdom. (Zugravu et al, 2008) Using the exponential smoothing method, the smoothing constant obtained is 0.693 and the minimum mean square error obtained is 0.049. This method was chosen as the forecasting method because there are no seasonality in the data found and also because the data seems to be fluctuating around a straight lined trend. Moreover, using a minimized mean square error ensures a better accuracy of the forecast and also smoothes out the fluctuations observed on the graph. Based on this method, the forecasted carbon dioxide emission for the year 2008 obtained is 8.886 metric tons per capita. Nevertheless, there are limitations to this method of forecasting. For example, this method is not very suitable for long term forecasting, that is forecasting over the span of two years. This is evident in how we are only able to obtain the forecast of an extra year as we are unable to find the error without the actual data of the forecasted year. Since the error cannot be obtained hence the forecast for the following year (i.e. 2009) cannot be found. In fact, the forecasted data may not be that reliable either due to the following reasons. Firstly, we cannot assume that only one factor is affecting the......

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...A Time Series Forecasting Analysis on the Monthly Stocks of Rice in the Philippines A Research Paper Presented To Dr. Cesar Rufino Of the Department of Economics School of Economics De La Salle University, Manila In Partial Fulfillment of the Course Requirements in Economic Forecasting (ECOFORE) Term 3 AY 2014-2015 Submitted by: Jayme, Kevin Matthew D. April 24 2015 0 I. Introduction The Philippines has been the accredited as an agricultural nation that provides different types of agricultural related goods, both for the domestic and international market. Rice has been the staple food in the Philippine to 80% of the population as it is customary diet that has been in beaded in the Philippine culture (Drilon Jr., 2012). Despite the strong history of agriculture and the skills and weather condition perfect for growth of rice, decrease of land and increase of total population around the Philippines decrease the opportunity for the population to have access to rice. In addition, neighboring countries, such as Thailand and Vietnam, had been on the rise of rice exportation. Not to mention the implementation of the ASEAN integration is happening in 2015. This means that the Philippines is lagging behind as it is the 8th largest exporters of rice in the world (Tiongco & Francisco, 2011). Institution, such as International Rice Research Institute (IRRI), has gone into research and development of rice growth in different conditions and......

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...Choose one of the forecasting methods and explain the rationale behind using it in real life. I would choose to use the exponential smoothing forecast method because it weighs the most recent past data more strongly than more distant past data. This makes it so that the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be in higher demand in recent times than past. Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. By looking at the data of other companies the fast food chain would be able to put together a forecast to determine if their business venture was viable. They could examine the sales data and determine through a exponential smoothing forecast if it made sense for them to enter into the market. This would show the trends and changes in the data more recently rather than in past time. 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 is based on using historical data to predict future behavior. This method could be used by a retail store, fast food restaurant or clothing manufacturer to predict sales for an upcoming season change. The causal model uses a mathematical......

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.... Applied Time Series Econometrics. : Cambridge University Press, . p 1 http://site.ebrary.com/id/10131743?ppg=1 Copyright © Cambridge University Press. . All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. . Applied Time Series Econometrics. : Cambridge University Press, . p 2 http://site.ebrary.com/id/10131743?ppg=2 Copyright © Cambridge University Press. . All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. . Applied Time Series Econometrics. : Cambridge University Press, . p 3 http://site.ebrary.com/id/10131743?ppg=3 Copyright © Cambridge University Press. . All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. . Applied Time Series Econometrics. : Cambridge University Press, . p 4 http://site.ebrary.com/id/10131743?ppg=4 Copyright © Cambridge University Press. . All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. . Applied Time Series Econometrics. : Cambridge University Press, . p 5 http://site.ebrary.com/id/10131743?ppg=5 Copyright © Cambridge University Press. . All rights reserved. May not be......

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...Time Series and Forecasting Learning Team A Quantitative Reasoning for Business/501 August 23, 2011 Dr. Champion Time Series and Forecasting The purpose of this paper is for statisticians from the accountant department of Norton Company to compute the quarterly seasonal index for the years of 2003 through 2006 by using the ratio-to-moving-average method. In addition, the accountants will deseasonalize the data and determine the trend equation. Furthermore, the statisticians will estimate Norton Company’s seasonally adjusted sale for the four quarters of 2007. The quarterly sales for the Norton Company were given in millions of dollars for four years. Therefore, the recorded quarterly sales for the Norton Company were referenced and included 16 quarters total. A scatter plot was graphed showing the recorded sales for the 16 quarters. The following equation was obtained from the historical data: y = 0.461x + 4.2 with the R² of 0.1695 and a R of 0.412. R is the coefficient of correlation, which provides the strength and direction of a linear relationship. R2 is the coefficient of determination, which measures the amount of variation in y that can be explained by the variation in x for example: 0 ≤ R2 ≤ 1. With this unadjusted regression equation and R² we can see that there is seasonality. Accordingly, a higher R² is required to prove that our equation is accurate and good for forecasting. The first step is to find the moving total....

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...Time series analysis We are pleased to submit the following report on the “Time Series Analysis”. By completing the report, we have got acquainted importance and relevance of time series on business application. We also perceived idea on the whole process of Time Series Analysis. We acquired knowledge about the method of measuring trend, growth rate, acceleration rate etc. In spite of limitation of time & opportunity we have tried our level best to complete the report. We are pleased to provide you with this report with necessary analysis, references and we shall be available for any clarification, if required. Thank you for assigning us in this study. On behalf of the group Md. Arif Hasan ID: 12-150 Table of Contents Serial No Topic Page No 1 Letter of Transmittal 1 2 Rationale of the study 2 3 Objectives of the report 3 4 Methodology of the report 3 5 What is Time Series 4 6 Uses of Time Series in Business 5 7 Components of a Time Series 5 8 Classical Time Series Model 9 9 Methods of trend measurement 9 10 Least squares method 10 11 The Growth Rate 14 12 The Acceleration Rate 15 13 Rule of 72 16 14 Bibliography 17 Rationale of the study Having been assigned to prepare a report on Time Series Analysis we are submitting the term paper based on our findings and understandings. Time series analysis has vast application and is of huge importance in the field of Business and Economics as well as in decision making thereof. Calculating secular trend we......

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...Applying Time Series Methodologies Derek Griffin RES/342 March 22, 2012 Olivia Scott Applying Time Series Methodologies MEMO To: Myra Reid, VP of Production From: Derek Griffin, Market Analyst Date: 22 March 2012 Subject: Three Week Analysis Simulation to Predict Blues Inc. Forecast Message: Over the past three weeks an indebt research analysis was conducted to provide Blues Inc reasonably accurate forecast that will ensure continued growth to the six percent market share of a 45 billion dollar industry. In week one the marketing team was given a directive from the Chief Executive Officer, Barbara Baderman, to have an effective advertising strategy in place to become the industry leader. A regression analysis was performed using sales as the selected variable for the strong positive relationship to advertising budget. The correlation coefficient of sales with the advertising budget is 0.96, which was higher than the relationship of competitors advertising budget or retail coverage. Sales with a lower standard error indicate a better predicted forecast. Using the regression equation and expected sales of 2,400 million, the forecasted advertising budget should be set at 162 million. During week two the marketing team was challenged to predict the market sales for the next year. Denim sales have increased five percent over the past four years and is expected to increase again next year. The team used the weighted moving average with a weight of .9 for......

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...Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes. To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available. In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include: Trends – long term movements in either direction Cycles - wavelike variations lasting more than a year usually tied to economic or political......

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