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Unformatted text preview: Forecasting Forecasting SUMMARIZING Today Forecasting: 1. Long term : not much known, numerical data not available. Use of judgment technological forecasting methods 2. Short Term: Forecasting quantitative models if data allows. 3. Quantitative: Explanatory F= f (X 1 , X 2 , X 3 .) Time series: F T = f { A T1 , A T2 , A T3 , } 4. Cross sectional data vs Time series data. 5. Mathematical method for seasonality 6. Error Analysis: Residuals and error minimization (MSE &amp; MAPE) Assumes causal system past ==&gt; future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases Ceteris Paribus assumption in Time series. I see that you will get an A this semester. 1. Averaging methods: Moving Averages smoothing Weighted Moving Averages smoothing Exponential Smoothing 1. Trend analysis : Linear trend equation, Trend adjusted Exponential sm 1. Causal methods: Simple linear regression 3. Time Series Decomposition seasonality Forecasting _ Anchor Forecasting _ Anchor SUMMARIZING: Product Demand Charted over 4 Years with Trend and Seasonality Year 1 Year 2 Year 3 Year 4 Seasonal peaks Trend component Actual demand line Average demand over four years Demand for product or service Random variatio n Naive Approach Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then June sales will be 48 Sometimes cost effective &amp; efficient 1995 Coel Corp. MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time Equation MA MA n n n n = = Demand in Demand in Previous Previous Periods Periods Moving Average Method Youre manager of a museum store that sells historical replicas. You want to forecast sales (000) for 2003 using a 3period moving average. 1998 4 1999 6 2000 5 2001 3 2002 7 Moving Average Example Moving Average Solution Time Response Y i Moving Total (n=3) Moving Average (n=3) 1998 4 NA NA 1999 6 NA NA 2000 5 NA NA 2001 3 4+6+5=15 15/3 = 5 2002 7 2003 NA Moving Average Solution Time Response Y i Moving Total (n=3) Moving Average (n=3) 1998 4 NA NA 1999 6 NA NA 2000 5 NA NA 2001 3 4+6+5=15 15/3 = 5 2002 7 6+5+3=14 14/3=4 2/3 2003 NA Moving Average Solution Time Response Y i Moving Total (n=3) Moving Average (n=3) 1998 4 NA NA 1999 6 NA NA 2000 5 NA NA 2001 3 4+6+5=15 15/3=5.0 2002 7 6+5+3=14 14/3=4.7 2003 NA 5+3+7=15 15/3=5.0 95 96 97 98 99 00 Year Sales 2 4 6 8 Actual Forecast Moving Average Graph TimeSeries Methods TimeSeries Methods Simple Moving Averages Simple Moving Averages Week 450 430 410 390 370       5 10 15 20 25 30 Actual patient arrivals Patient arrivals TimeSeries Methods TimeSeries Methods Simple Moving Averages Simple Moving Averages Actual patient arrivals 450 430 410 390 370 Week       5 10 15 20 25 30 Patient arrivals TimeSeries Methods...
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This note was uploaded on 03/03/2011 for the course MARKETING 101 taught by Professor Singh during the Spring '11 term at Management Development Institute.
 Spring '11
 Singh

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