The document discusses forecasting room sales using Holt Winter's method. It explains that Holt Winter's method uses historical time series data and accounts for trends and seasonal factors to forecast future values. Specifically, it forecasts rooms sold (Ft) by adding the trend (Tt) and seasonal factors (St) to the baseline (Bt) over time using the formulas: Bt = alpha(Yt-St-p)+(1-alpha)(Et-1 + Tt-1), Tt = beta(Et - Et-1) + (1-beta)Tt-1, and St = Lambda(Yt-Et) + (1-Lambda)St-p. The document also provides the 2016 forecast results and

1. Forecast
Using Holt Winter’s Method

2. Holth Winter's Method
• Historical Time Series
• Read Trend and Seasonal Factors

3. Holts Winter’s Method Explained
Forecast reached by adding trend with seasonal
factors
Ft = Bt + Tt + St

4. Forecast Formulated
• The forecasts counted using time series data from
April 2014 - November 2015
• the data used compiled in weekly datas

5. Existing Data
0
450
900
1350
1800
10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 2 5 8
Room Sold 2015 Room Sold 2014
0
450
900
1,350
1,800
10
15
20
25
30
35
40
45
50
2
7
12
17
22
27
32
37
42
Week #

6. Formulas
Bt = alpha(Yt-St-p)+(1-alpha)(Et-1 + Tt-1)
Tt = beta(Et - Et-1) + (1-beta)Tt-1
St = Lambda(Yt-Et) + (1-Lambda)St-p

7. Forecast Result
2016 Forecast
NB: This Forecast didn’t
include Air Crew Occupancy

8. Plus and Minus
PLUS
• This forecast include
seasonality factors
• Accurate for annual forecast
• Trend is also calculated
MINUS
• Not too accurate with short
term forecast
• Can’t calculate booking
pace and lead time
• Need longer history data to
be more precise.

9. Improvement
• Combine quantitative analysis with qualitative to get
more precise result.
• If longer historical data provided day per day forecast
can be counted precisely
• Can be used in segmented market to get each market
trend and seasonality patterns