...Forecasting with Indices Christopher L Kearney University of Phoenix QRB/501 Quantitative Reasoning for Business Maryam Boluri May 2, 2011 Forecasting with Indices This writer will begin by defining forecast and index while detailing the importance of both as they relate to the makeup of any company. This type of data can be financial or non-financial depending on what the company offers. Forecasting is a method used by companies to predict current and future trends. Many companies have realized this to be the backbone of the company because it predicts whether or not a company will break even and if a company does not break even decided whether the company will be up and running the following year. An index is a point of reference concerning numbers with common points. Indices are used to observe historical and short-term comparisons with percentages change commonly used. This week’s lesson entailed obtaining the Summer Historical Inventory Data from the materials area in the week two forum and converting the information into an index. The time series information is to be used to forecast the inventory needed for the upcoming year. To give a company a better view of making decision a month-to-month forecast is best because more information can be obtained over a shorter period. The Summer Historical Data obtained from University of Phoenix Material was converted into an index using Microsoft excel software. The data showed typical demand for summer highs using...
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...Forecasting with Indices Jamie Prather QRB/501 April 23rd, 2012 Measuring the winter historical inventory data for the next 12 months consists of preparing a simple exponential forecast method to determine the method of simple average during the 12 separate indices. The forecast was processed by Running mean Absolute Deviation (RMAD) was computed by taking the average of two one-year-wide averages that are offset by one period relative to each other. Computing the ratio divided by the moving average in each period. Running sum of forecast errors were computed by taking the differences between the actual and the forecast demand for the periods being evaluated. | | | | | | Alpha | 0.2 | | | | Month | 1 | 2 | 3 | 4 | | Forecast | ABS Value | RMAD | RSFE | TS | 1 | | 39800 | 32180 | 62300 | 189,480 | 189,480 | | | | | 2 | 57350 | 64100 | 38600 | 66500 | 226,550 | 189480 | 37070 | 37070 | 37,070 | 1 | 3 | 15400 | 47600 | 25020 | 31400 | 119,420 | 196894 | 77474 | 114544 | -40,404 | -0.35274 | 4 | 27700 | 43050 | 51300 | 36400 | 158,450 | 181399.2 | 22949.2 | 137493.2 | -63,353 | -0.46077 | 5 | 21400 | 39300 | 31790 | 16800 | 109,290 | 176809.4 | 67519.36 | 205012.6 | -130,873 | -0.63836 | 6 | 17100 | 10300 | 31100 | 18900 | 77,400 | 163305.5 | 85905.49 | 290918 | -216,778 | -0.74515 | 7 | 18000 | 45100 | 59800 | 35500 | 158,400 | 146124.4 | 12275.61 | 303193.7 | -204,502 | -0.67449 | 8 | 19800 | 46530 | 30740...
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...Forecasting Using Indices Tricia Thedford QRB 501/Quantitative Reasoning for Business March 4, 2012 Vinata Kulkarni, PhD Forecasting using indices Forecasting is an integral part of business. Forecasting allows investors to see anticipated growth/decline in a business. An accurate forecast will also allow a business to respond appropriately should a problem be evident in the desired business plan. Forecasting can be done on all data within a financial statement or can target specific areas, depending on the information desired. Investors as well as business executives have a need to see where a company is headed when making future business decisions. The dictionary defines the word forecast as “to anticipate, calculate, or predict (some future event or condition) usually as a result of rational study and analysis of available pertinent data” (Merriam-Webster, Inc., 2002, para 2). One should review all data available for making an accurate business forecast. In researching Dell, Inc., inventory history the following data was obtained: Dell, Inc. historical inventories data | 2011 | 2010 | Period | Amount | Period | Amount | October 31 | 1.40 billion | October 31 | 1.29 billion | July 31 | 1.35 billion | July 31 | 1.37 billion | April 30 | 1.28 billion | April 30 | 1.18 billion | January 31 | 1.30 billion | January 31 | 1.05 billion | 2009 | 2008 | Period | Amount | Period | Amount | October 31 | 952 million | October 31 | 1.11 billion | July...
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...QRB/501 – Quantitative Reasoning for Business University of Phoenix Inventory Proposal This proposal is for a solution for an inventory problem within an organization. The details of the organization’s business operations are established and the problem identified so a potential solution may be formulated and proposed. The expected benefits to the organization are also examined as the motivation for implementation. Additionally, an inventory index is used to compare and contrast historical trends leading to the inventory problem and supporting evidence is presented through a histogram and slope-intercept calculations. Organization Vertical Entertainment (VE) is a fireworks wholesaler headquartered in Austin, Texas. VE has been in business since 2005 and provides fireworks products through business-to-business and business-to-consumer channels. The organization’s key segments are the small-box retailers and fireworks display companies. The small-box retailers account for increased volume during the summer months (e.g. 4th of July) whereas the fireworks display companies purchase goods year-round. In recent years VE has expanded to direct sales via the company website. Inventory Problem VE purchases all firework inventories directly from China and they arrive in roughly 12 weeks. Product backorder usually result in lost sales because most events are time specific. The main inventory issue...
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...Inventory Proposal: Analysis of University Of Phoenix Demand for Units QRB501 Inventory Proposal University of Phoenix is a for-profit university initiated in 1976 by John Sperling (Kinser, 2006). The university originated in the Phoenix metropolitan area and started with only eight students (Kinser, 2006). Sperling wrote about the origin of University of Phoenix and refers to it as “Rebel with a Cause” (Kinser, 2006, para. 2). Sperling experienced much opposition in getting UOPX in operation to help underserved and hard working adults further their education. Thanks to his determination and confrontational side, Sperling is 88 and can see UOPX having a net worth of $9 billion (Kinser, 2006). University of Phoenix is top on the list of for-profit universities. They enroll more than 230,000 students and have 170 throughout the United States (Kinser, 2006). It is apparent to past critics that universities who provide opportunities for working adults are growing at a rapid rate. More students are leaning to online schools and universities to advance with higher degrees. One thing plaguing the United States is the economy. The cost of tuition is steadily increasing, making it more difficult for students to afford education without the help of scholarships, grants, and student loans. It is said that for-profit universities depend heavily on federally funded students to stay afloat financially (Field, 2011). There are reports and suits filed from past instructors...
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...Forecasting and Indices Forecasting is a process in which statements or conclusions are made regarding the outcome of events that have not yet happened. Forecasting is predicting what the could look like. There are many examples of forecasting. Estimating or predicting can be referred to as formal statistical methods that employ time series, cross-sectional or longitudinal data. Forecasting can be use to estimate a wide variety of issues, weather related events, the use of resources, sales, profit or loss and even staffing needs for a particular project. Coupling these findings with good planning can give a company a good indication of what the future should look like. Forecasting can use many different methods. Evaluating your objectives and conditions relevant to the individual situations can help in choosing the correct method to use. Another option is to use several methods and compare or combine the results, as many times, there is no one “best” method. “The forecast error is the difference between the actual value and the forecast value for the corresponding period. where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t. Measures of aggregate error: Mean absolute error (MAE) Mean Absolute Percentage Error (MAPE) Mean Absolute Deviation (MAD) Percent Mean Absolute Deviation (PMAD) Mean squared error (MSE) Root Mean squared error (RMSE) Forecast skill (SS) Average of Errors (E)...
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...Inventory Proposal Inventory should be closely followed especially for companies that rely heavily on seasonal sales. Companies will often times use forecasting models to predict future sales and the amount of inventory needed to make their sales quotas. SSMA retail outlet will evaluate their current data from the past four years to identify any inventory issues by using indices to analyze the data and histograms to forecast future inventory cost saving models. SSMA is a small retail outlet store that sells a variety of summer clothing throughout the year. The new owners have decided to install an inventory system to keep better track of the inventory, however, a complete analysis of the previous data must be completed and a forecasting model must be completed in order to determine how much inventory will be needed in order to increase net profit. The store is located in an outlet mall that relies heavily on tourists; therefore, sales are very seasonal. The summer data shows that they have an uptick in inventory during the high summer months and the owners should determine by using indices and histogram models to assist in predicting future sales. Inventory management problem SSMA past data will help predict trends and assist in decision making processes to determine inventory loss, inventory shelf life, future sales and or clearance items. This type of information is critical and valuable for small retail stores and will need this data in order to recognize key areas...
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...Forecasting with Time Series 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...
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...corrective actions or not. Traditional EVM does not reflect the actual schedule performance at the project last third since at the project end, schedule performance index will reach 1 and all budget will be earned. This is affecting the creditability of schedule prediction and expected cost at completion. In this regard, (Lipke, 2005) has proposed an enhancement to the traditional EVM by using the concept of Earned Schedule (ES). The Earned Schedule (ES) had improved the reliability of the indicators for the whole project including early and late start despite EVM which is limited to early finish only. ES focuses on cost and schedule using EVM while EVM is focused on cost only. ES is increasing the forecasting reliability as the project progresses to the end. Testing earned schedule forecasting reliability on real and simulated projects shows that applying earned schedule (ES) has significantly increased the outcome reliability (Lipke,...
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...Jiang 1 Forecasting Imports Price Growth in China With Many Potentially Relevant Predictors: An Analysis of Forecast Methods Lili Jiang Saint Louis University ECON 698 Professor: Hailong Qian Jiang 2 Abstract This paper examines to forecast monthly growth rate of import price in China in the presence of 19 potentially relevant predictors, including international trade related variables, broad economic trend measurement, world CPI and world commodity prices and indices from January 1994 to January 2013. Simulated out-of-sample autoregressive model serves as the benchmark model. Individual autoregressive distributed lag (ARDL) model, simple averaging combination forecast, approximate Bayesian model averaging combination forecast, discount MSFE combination forecast and diffusion indices forecast are built to compete the benchmark model. We found that all combination forecasts outperform benchmark forecast but are not statistically significant by using Clark & West test. The ARDL model with the predictor of China export price and China PPI beat the benchmark model significantly. Finally, resorting to Diebold and Mariano test, we found that the predictor of PPI in China performs best in forecasting the growth rate of import price in China. Jiang 3 1. Introduction In the past 20 years, China has undertaken market oriented economic reforms and achieved an average annual growth rate of 9.62%. The expansion of economic...
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...Briefly describe the steps that are used to develop a forecasting system. Identify the three forecasting time horizons and state an approximate duration for each. Provide a Criminal Justice example for each time horizon. Forecasting is defined as the art and science of predicting future events (Heizer & Render, 2008). When implementing a forecasting system, there are seven basic steps, determine the use of the forecast, select the items to be forecasted, determine the time horizon of the forecast, select the forecasting model(s), gather the data needed to make the forecast, make the forecast, and validate and implement the results. (Heizer & Render, 2008) A forecast is usually classified by the future time horizon that it covers (Heizer & Render, 2008). According to Heizer & Render (2008) “Time horizons fall into three categories: 1. Short-range forecast: This forecast has a time span of up to 1 year but is generally less than 3 months. It used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels. An example would be placing resource in areas known as “hot spots” that have been identified thru crime analysis. 2. Medium-range forecast: A medium-range, or intermediate, forecast generally spans from 3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans. An example would be the planning of manpower for a new...
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...Institute of Management Sciences Peshawar Bachelors in Business Studies Course Plan Course Title: Statistics for Business Instructor: Shahid Ali Contact Email shahid.ali@imsciences.edu.pk Semester/Duration: 16 Weeks Course objectives : To introduce students to the concepts of statistics and to equip them with analytical tools to be used in business decision making. The course is intended to polish the numeric ability of the students to identify business problems, describe them numerically and to provide intelligible solutions by data collection and inferential principles. Course pre-requisites Intermediate statistics Attendance Policy: Late arrivals are highly discouraged. Any student coming late to a class late by 5 minutes after the scheduled start time will be marked as absent for the day. The teacher reserves discretion, however, to allow or disallow any student, to sit in the class in case of late arrivals. Attendance is not be entertained once the attendance register is closed. Class Project Students will be divided in groups for a class project. Each group will have to nominate a group leader. The details of the project will be made available to the group leader. Class Presentations Each student will have to make at least one individual presentation and one group presentation in the class. The group presentation will be on the project explained earlier. The individual presentations will...
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...a p p e n d i x C DEMAND FORECASTING IN MARKETING * When you finish this appendix you should • Understand the principles of forecasting. • Know the differences between Time Series and Regression Analyses. • Understand how expert opinion, sales forces and consumer opinions can all contribute to better forecasts. * by Beverley Thompson, The University of Western Sydney, Nepean Demand Forecasting in Mar keting w 689 An important part of the marketing planning process is the setting of goals that are realistic and achievable, given a particular marketing environment and level of marketing commitment. In marketing, such goals are usually based on market share objectives and sales targets, both of which require accurate forecasts of total market size, market size of target segments and likely market share within a targeted segment. W H AT A R E W E F O R E C A S T I N G ? Accurate forecasting requires a clear definition of the market in question. Markets may be differentiated on the basis of the following variables. GEOGRAPHY A market may be defined at world, country, state, region, sales territory, town, store or customer level. When formulating a forecast or other marketing plans, the geographical dimension must be clearly indicated. Planning Coca Cola consumption for the year 2000 Sydney Olympics for example, will necessitate the forecasting of increased consumption for the Sydney sales region, but not necessarily for Brisbane...
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...4/13/2015 Forecasting Research Papers Hapikampr Login Join The Research Paper Factory Join Search Browse Saved Papers Search over 100,000 Essays Home Page » Business and Management Forecasting In: Business and Management Forecasting Forecasting HSM/260 University of Phoenix 06/20/2013 Exercise 9.1 The following data represent total personnel expenses for the Palmdale Human Service Agency for past four fiscal years: 20X1 $5,250,000 20X2 $5,500,000 20X3 $6,000,000 20X4 $6,750,000 Forecast personnel expenses for fiscal year 20X5 using moving averages, weighted moving averages, exponential smoothing, and time series regression. For moving averages and weighted moving averages, use only the data for the past three fiscal years. For weighted moving averages, assign a value of 1 to the data for 20X2, a value of 2 to the data for 20X3, and a value of 3 to the data for 20X4. For exponential smoothing, assume that the last forecast for fiscal year 20X4 was $6,300,000. You decide on the alpha to be used for exponential smoothing. For time series regression, use the data for all four fiscal years. Which forecast will you use? Why Moving Averages Fiscal Year Expenses Please login to view the full essay... Essay's Statistics Submitted by: hapikampr Date shared: 08/07/2013 10:35 AM Words: 707 Pages: 3 20X2 $ 5,500,000 20X3 6...
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...Test of asymmetric Garch models Henri Högkulla s082880 Carl-Anton Karlsson s081760 Hanken School of Economics Department of Finance and Statistics Vaasa November 2011 Contents Abstract ...................................................................................................................................... 1 1 Introduction ............................................................................................................................. 2 2 Methodology ........................................................................................................................... 4 2.1 ARCH and GARCH ......................................................................................................... 4 2.2 EGARCH ......................................................................................................................... 4 2.3 GJR-GARCH ................................................................................................................... 5 2.4 Distributions ..................................................................................................................... 5 2.5 Information criterions ....................................................................................................... 6 3 Data ......................................................................................................................................... 7 3.1 Descriptive statistics .............................................
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