Forecasting - Introduction

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Presentation transcript:

HMP 654 Operations Research and Control Systems in Health Care Spring/Summer 2016

Forecasting - Introduction Forecasting in Health Care Forecasting Models Structural Models Time Series Models Expert Judgment Time Series Models: Demand has exhibited some measurable structure in the past. The structure will continue into the future.

Forecasting - Time Series Signal vs. Noise Extrapolation Models Accuracy of Forecasts

Forecasting - Stationary Models Stationary Time-Series Moving Averages

Forecasting - Moving Avgs.

Forecasting - Moving Avgs. 33 + 38 2 38 + 31 2 33 + 38 + 31 + 35 4 SUMXMY2(B7:B26,D7:D26)/COUNT(D7:D26)

Forecasting - Moving Avgs.

Forecasting - Weighted M.A. Weighted Moving Averages

Forecasting - Weighted M.A. 0.3 x 33 + 0.7 x 38 0.3 x 38 + 0.7 x 31

Forecasting - Weighted M.A Finding the Optimal Weights

Forecasting - Weighted M.A. Finding the Optimal Weights MSE vs W2 W2

Forecasting - Weighted M.A. Finding the Optimal Weights

Forecasting - Weighted M.A. Finding the Optimal Weights

Forecasting - Exp. Smoothing Exponential Smoothing

Forecasting - Exp. Smoothing 0.7 x 33 + 0.3 x 33 0.7 x 38 + 0.3 x 33

Forecasting - Exp. Smoothing

Forecasting - Trend Models

Forecasting - Holt’s Method Compute the base level Et for time period t using equation 11.6 Compute expected trend value Tt for time period t using equation 11.7 Compute the final forecast Y^t+k for time period t+k using equation 11.5

Forecasting - Holt’s Method Initial base level = first demand value Set initial trend to 0 Forecast for Qtr. 3, 1990: 634.2= 0.5 x 584.1 + (1 - 0.5) x (684.2 + 0) -25 = 0.5 x (634.2 - 684.2) + (1 - 0.5) x 0 609.1 = 634.2 + 1 x (- 25)

Forecasting - Regression Linear Trend Model

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Forecasting - Regression

Forecasting - Regression Linear Trend Model

Forecasting - Regression Quadratic Trend Model

Forecasting - Regression

Forecasting - Regression Quadratic Trend Model

Forecasting - Seasonality Adjusting trend predictions with seasonal indices 102 + 107 + 106 + 108 + 106 5

Forecasting - Seasonality

Forecasting - Seasonality Use of Seasonal Indices Create a trend model and calculate the estimated value for each observation in the sample. For each observation, calculate the ratio of the actual value to the predicted trend value For each season, compute the average of the ratios calculated in step 2. These are the seasonal indices. Multiply any forecast produced by the trend model by the appropriate seasonal index calculated in step 3.

Forecasting - Seasonal Regression Models

Forecasting - Seasonal Regression Models