1. FORECASTING
16-Jan-19
Dr.B.Sasidhar

2. Definition
Forecast is a prediction of future events used for planning process.
16-Jan-19
Dr.B.Sasidhar

3. Introduction to Forecasting
What is forecasting?
Primary Function is to Predict the Future
Why are we interested?
Affects the decisions we make today
Examples: who uses forecasting in their jobs?
forecast demand for products and services
forecast availability of manpower
forecast inventory and materiel needs daily

4. Forecasting Methods Subjective Forecasting Methods
Measures individual or group opinion
Objective Forecasting Methods
Forecasts made based on past history and a model.
Time Series Models: use only past history.
Easy to incorporate into computer models.
Regression: Causal Model to predict

5. Objective Forecasting Methods
Two primary methods: causal models and time series methods
Causal Models
Let Y be the quantity to be forecasted and (X1, X2, , Xn) be n variables that have predictive power for Y.
A causal model is Y = f (X1, X2, , Xn).
“Y depends on X1, X2, , Xn”
A typical relationship is a linear one. That is,
Y = a0 + a1X an Xn.

6. Causal Methods Linear Regression
Deviation,
or error {
Dependent variable
Independent variable X Y
Regression
equation:
Y = a + bX
Estimate of
Y from
regression
equation
Actual
value
of Y
Value of X used
to estimate Y
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Dr.B.Sasidhar 8

7. Time Series Methods Trend Seasonality Cycles Randomness
A time series is just collection of past values of the variable being predicted. Also known as naïve methods. Goal is to isolate patterns in past data. (See Figures on following pages)
Trend
Seasonality
Cycles
Randomness