Forecasting Models

1. Forecasting Models Static vs. Dynamic

2. Basics

3. Visual Investigation

4. Static Model , Simple Average Basic Assumption: No change in demand, all fluctuations are random. Forecast = Average A measure of match between forecast and actual data is MAD = Mean Absolute Deviation = Average (absolute value of (sales-forecast) for the overall data range)

5. Static Model � Modeling Trend Assumption : There is a linear trend in data. Plot the data and add trend line (linear). Intercept Slope = Trend First Period Forecast = Intercept + Trend Forecast = Previous period forecast + Trend

6. Static Model with SeasonalitySeasonality*(Linear Trend)

7. Dynamic Model � Exponential Smoothing

8. Exponential Smoothing with Trend First Period Forecast = Intercept + Slope (from trendline) Next Period Forecast Average = (1-a)Previous Forecast + a*Previous Sales Slope = b* D in Averages +(1-b)*Previous Trend Forecast = Average +Trend To incorporate seasonality multiply forecast with seasonality factor.

Forecasting Models

Forecasting Models

Presentation Transcript

Sequential Demand Forecasting Models

Topic 5 Forecasting Models

Bayesian Forecasting and Dynamic Models

Forecasting Models With Linear Trend

Forecasting Models

Statistical Models for Probabilistic Forecasting

Sample enumeration: Forecasting from statistical models

Regression and Forecasting Models

FORECASTING MODELS AND METHODS

Other Factors: Bias of Forecasting Models

FORECASTING MODELS AND METHODS

Forecasting Models With Linear Trend

Forecasting Models – Chapter 2

Hydrologic Forecasting With Statistical Models

Distributed Models and Flash Flood Forecasting

Forecasting using ARIMA-models

Forecasting Models

Forecasting using simple models

Statistical Forecasting Models

Download Financial Forecasting Models

Forecasting Models