Ch19_-_L&O_Forecast_Models - CHAPTER 19 Forecasting...

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1 of 79 Forecasting Forecasting Models Models CHAPTER 19
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2 of 79 Introduction to Time Series Forecasting Forecasting is the process of predicting the future. Forecasting is an integral part of almost all business enterprises. Examples Manufacturing firms forecast demand for their product, to schedule manpower and raw material allocation. Service organizations forecast customer arrival
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3 of 79 More examples Security analysts forecast revenues, profits, and debt ratios, to make investment recommendations. Firms consider economic forecasts of indicators (housing starts, changes in gross national profit) before deciding on Introduction
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4 of 79 Good forecasts can lead to Reduced inventory costs. Lower overall personnel costs. Increased customer satisfaction. The forecasting process can be based on: Educated guess. Expert opinions. Past history of data values, known as a Introduction
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5 of 79 The goal of a time series forecast is to identify factors that can be predicted. This is a systematic approach involving the following steps. Step 1: Hypothesize a form for the time series model. Step 2: Select a forecasting technique. Step 3: Prepare a forecast. Steps in the Time Series Forecasting Process
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6 of 79 Step 1: Identify components included in the time series Collect historical data. Graph the data vs. time. Hypothesize a form for the time series model. Verify this hypothesis statistically. Steps in the Time Series Forecasting Process
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7 of 79 Step 2: Select a Forecasting Technique Select a forecasting technique from among several techniques available. The selection includes Determination of input parameter values Performance evaluation on past data of each technique Step 3: Prepare a Forecast using the selected technique Steps in the Time Series Forecasting Process
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8 of 79 Components of a Time Series Long-term trend A time series may be stationary or exhibit trend over time. Long-term trend is typically modeled as a linear , quadratic or exponential function. Seasonal variation When a repetitive pattern is observed over some time horizon, the series is said to have seasonal behavior. Seasonal effects are usually associated with calendar or climatic changes. Seasonal variation is frequently tied to yearly cycles.
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9 of 79 Cyclical variation An upturn or downturn not tied to seasonal variation. Usually results from changes in economic conditions. Cyclic variation usually takes place over a series of years Random effects These effects are whatever is left over that are either: Results of exogenous events Just not explainable Components of a Time Series
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10 of 79 A stationary time series Linear trend time series Linear trend and seasonality time series Tim e Time series value Future Components of a Time Series
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11 of 79 In a stationary (horizontal) model the mean value of the time series is assumed to be constant.
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This note was uploaded on 01/20/2010 for the course MNS 407 taught by Professor Rosbrook during the Spring '10 term at National University of Health Sciences.

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Ch19_-_L&O_Forecast_Models - CHAPTER 19 Forecasting...

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