The document discusses quantitative forecasting techniques, specifically Winter's exponential smoothing. It explains that Winter's exponential smoothing is used for time series data that exhibit both trends and seasonality. The model uses four equations to calculate the smoothed series level, trend estimate, and seasonal component estimate. The forecast is then determined by taking the smoothed level, trend, and seasonal component for the specified number of periods into the future.

1. Forecasting Sales
With Winter’s Exponential Smoothing
Nikhil Bhagadia

2. Importance of Forecasting
 Sales : Forecasting is the process by
which people ponder and prepare for
the future.
 Marketing : Total demand for products
must be forecasted in order to plan
total promotional effort.

3. Types of Forecasting
Techniques
 Qualitative Forecasting Methods
 These Methods are used when historical data are scarce or not
available at all.
 Based more on expert opinion to predict future.
 Used in Sales of new product, etc.
 Quantitative Forecasting Methods
 These Methods are used when historical data are available.
 Advantage :-
 Reproducible by any forecaster
 Based on approved techniques and model available.
 Full proof and reliable.

4. Quantitative Forecasting
Methods

5. What is a Time Series?
 A time series is a series of
observations on a particular variable
collected over a period of time (usually
at equally spaced intervals).
Time Series Components
Trend Cyclical
Seasonal Irregular

6. Cyclical
Trend Component
Component
 Overall upward or downward  Upward or downward swing
movement
 Data taken over a period of
years
Seasonal
Irregular Component
Component
 Upward or downward swing  Erratic, nonsystematic, random,
fluctuations
 Short duration and non-
repeating

7. Winter’s Exponential
Smoothing
 Winter’s exponential smoothing model
is the second extension of the basic
Exponential smoothing model.
 It is used for data that exhibit both
trend and seasonality.
 It is a three parameter model that is an
extension of Holt’s method.
 An additional equation adjusts the
model for the seasonal component.

8. Winter’s Exponential
Smoothing
 The four equations necessary for
Winter’s multiplicative method are:
 The exponentially smoothed series:
yt
LEVEL Lt (1 )( L t 1
bt 1
)
St s
 The trend estimate:
TREND bt ( Lt Lt 1
) (1 ) bt 1
 The seasonality estimate:
SEASONALIT yt
Y St (1 )St s
Lt

9. Winter’s Exponential
Smoothing
◦ Forecast m period into the future:
Ft m
( Lt mb t ) S t m s
 Lt = level of series.
 = smoothing constant for the data.
 yt = new observation or actual value in period t.
 = smoothing constant for trend estimate.
 bt = trend estimate.
 = smoothing constant for seasonality estimate.
 St =seasonal component estimate.
 m = Number of periods in the forecast lead period.
 s = length of seasonality (number of periods in the season)
 Ft m = forecast for m periods into the future.