are available to forecast time-series data that are stationary or that include no significant trend‚ cyclical‚ or seasonal effects. These techniques are often referred to as smoothing techniques because they produce forecasts based on “smoothing out” the irregular fluctuation effects in the time-series data. Three general categories of smoothing techniques are presented here: • Naive forecasting models are simple models in which it is assumed that the more recent time periods of data represent
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Forecast accuracy decreases as the time period covered by the forecast-the time horizon-increases. Steps in the Forecasting Process There are five basic steps in the forecasting process: 1. Determine the purpose of the forecast and when it will be needed. This will provide an indication of the level of detail required in the forecast‚ the amount of resources (manpower‚ computer time‚ dollars) that can be justified‚ and the level of accuracy necessary. 2. Establish a time horizon that the forecast must
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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. Forecast personnel expenses for fiscal year 20X5 using moving averages‚ weighted moving averages‚ exponential smoothing‚ and time series regression. Moving Averages Fiscal Year Expenses 20X2 $5‚500‚000 20X3 $6‚000‚000 20X4 $6‚750‚000 20X2-4 $18‚250‚000 20X5 $18‚250‚000/3 = $6‚083‚333 Weighted Averages Fiscal
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which forecasting technique is best used by the team. BankUSA Help Desk - Case Study The Help Desk of BankUSA is the primary customer contact unit within fiduciary operations. The department consists of 20 employees broken down into 14 full-time customer service representatives (CSRs)‚ 3 CSR support employees and 3 managers (Collier & Evans‚ 2013). The senior manager of the Help Desk‚ Dot Gifford‚ has established a team to address short-term forecasting. The Help Desk currently handles
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FORECASTING FUNDAMENTALS Forecast: A prediction‚ projection‚ or estimate of some future activity‚ event‚ or occurrence. Types of Forecasts * Economic forecasts * Predict a variety of economic indicators‚ like money supply‚ inflation rates‚ interest rates‚ etc. * Technological forecasts * Predict rates of technological progress and innovation. * Demand forecasts *
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Projected Total Sales of Sundance Direct Sales Calamba Branch in 2011 | A Term Paper for MGT 121 | Sandy Rose C. Nombrefia | Projected Total Sales of Sundance Direct Sales Calamba Branch in 2011 Introduction Billboards‚ signage and eye-catching advertisement paraphernalia of different direct selling companies are sprouting everywhere‚ either local or international. Many companies established names and compete to prolong their standing in the business world. Defined in businessdictionary
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research model is based on the assumption that Select one: there is a relationship between the time series and the dependent variable the independent variable is related to the dependent variable the variable being forecast is related to other variables in the environment there is a relationship between the time series and the independent variable e) the information is contained in a time series of data Which forecasting method is particularly good for determining customer preferences? Select
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barometers and thermometers for weather forecasters. In an attempt to forecast its future needs for mercury‚ Accuweather’s chief economist estimated average monthly mercury needs as: N = 500 + 10X where N = monthly mercury needs (units) and X = time period in months (January 2008= 0). The following monthly seasonal adjustment factors have been estimated using data from the past five years: Month Adjustment Factor January 15% April 10% July 20% September 5% December 10% (a) Forecast
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A Study on the Forecasted Sales of San Miguel Corporation for the year 2012 Redilyn Magbitang Ruth Anne Panganiban Lady Fatima Sandoval INTRODUCTION Background of the study San Miguel Corporation is the Philippines’ largest beverage‚ food and packaging company. The company now has more than 100 facilities around the Philippines and outside the country specifically Southeast Asia and China. One of the country’s premier business conglomerates‚ San Miguel’s extensive
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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 20 X 2‚ a value of 2 to the data for 20 X 3‚ and a value of 3 to the data for 20 X 4. For exponential smoothing‚ assume that the last forecast for fiscal year 20 X 4 was $6‚300‚000. You decide on the alpha to be used for exponential smoothing. For time series regression‚ use the data
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