Автор: Пользователь скрыл имя, 17 Января 2011 в 16:35, курсовая работа
Цель данной работы заключается в построении прогноза по статистическим данным индустрии гостеприимства собранным за несколько предыдущих лет и анализ прогноза на будущий период.
Задачи данной работы могут быть сформулированы следующим образом: раскрытие понятия о временных рядах и существующих в индустрии гостеприимства методах построения прогнозов; приведение конкретного примера с помощью программы Statgraphics Plus - анализ данных по ежемесячной загрузке гостиниц Северной Ирландии, выявление трендов и моделей сезонности, анализ случайности; построение прогноза с помощью функции автоматическое прогнозирование и анализ полученных данных с их дальнейшей трактовкой и выработкой конкретных рекомендаций и выводов по данной ситуации.
Введение…………………………………………………………….……………3
I. Теоретическое обоснование прогнозирования в индустрии гостеприимства и туризма
1.Сущность и методы прогнозирования…………………………….…….….5
2.Понятие временных рядов и основные этапы их анализа……………....…7
3.Общая характеристика STATGRAPHICS и его особенности………….....10
II. Анализ временных рядов в STATGRAPHICS…………………………..12
III. Автоматическое прогнозирование временных рядов………………...22
Заключение………………………………………………………………….…..31
Список использованной литературы………………………………………..32
Приложения………………………………………………………………….….33
12.00 31,0 30,247 0,752958
1.01 30,0 29,723 0,277028
2.01 38,0 37,2288 0,771169
3.01 38,0 38,6377 -0,637717
4.01 39,0 40,1892 -1,18921
5.01 46,0 45,1651 0,83485
6.01 53,0 47,9351 5,06485
7.01 45,0 46,3889 -1,38891
8.01 55,0 56,2029 -1,20291
9.01 50,0 51,5704 -1,57045
10.01 43,0 43,6224 -0,622415
11.01 39,0 39,5442 -0,544191
12.01 31,0 29,8471 1,15287
1.02 31,0 30,484 0,515986
2.02 39,0 37,851 1,14904
3.02 40,0 38,9717 1,02831
4.02 42,0 42,5214 -0,52141
5.02 50,0 47,9697 2,03026
6.02 51,0 50,6906 0,309364
7.02 46,0 47,0238 -1,02377
8.02 52,0 54,4967 -2,49673
9.02 50,0 50,6249 -0,624924
10.02 43,0 43,4146 -0,414596
11.02 38,0 39,5522 -1,55216
12.02 29,0 28,7317 0,268312
1.03 31,0 28,3721 2,62791
2.03 40,0 37,4046 2,59536
3.03 41,0 40,3735 0,626543
4.03 45,0 45,0982 -0,0981584
5.03 51,0 49,6939 1,30608
6.03 56,0 52,7907 3,20934
7.03 50,0 50,0473 -0,0473014
8.03 60,0 56,823 3,17697
9.03 57,0 56,3986 0,601406
10.03 50,0 48,3921 1,60787
11.03 44,0 44,8455 -0,845488
12.03 35,0 34,0508 0,94918
------------------------------
Period Forecast Limit Limit
------------------------------
1.04 33,011 29,5208 36,5012
2.04 39,5981 35,4729 43,7232
3.04 39,5781 34,7414 44,4148
4.04 43,2135 38,151 48,276
5.04 48,867 43,7112 54,0229
6.04 52,5199 47,2183 57,8215
7.04 46,5433 41,2064 51,8803
8.04 53,7401 48,3846 59,0955
9.04 51,2665 45,8767 56,6563
10.04 43,1365 37,7416 48,5314
11.04 39,6822 34,2832 45,0813
12.04 31,2978 25,8905 36,7051
1.05 30,7633 25,3545 36,172
2.05 38,4047 32,9959 43,8135
3.05 39,0692 33,6604 44,478
4.05 42,0929 36,6837 47,5021
5.05 48,8247 43,4155 54,234
6.05 52,3436 46,9344 57,7528
7.05 46,3901 40,9807 51,7995
8.05 53,4005 47,9912 58,8099
9.05 50,7164 45,3071 56,1258
10.05 43,2201 37,8107 48,6295
11.05 39,1526 33,7432 44,562
12.05 30,943 25,5336 36,3525
------------------------------
The StatAdvisor
---------------
This table shows the forecasted values for Occupancy rate. During
the period where actual data is available, it also displays the
predicted values from the fitted model and the residuals
(data-forecast). For time periods beyond the end of the series, it
shows 95,0% prediction limits for the forecasts. These limits show
where the true data value at a selected future time is likely to be
with 95,0% confidence, assuming the fitted model is appropriate for
the data. You can plot the forecasts by selecting Forecast Plot from
the list of graphical options. You can change the confidence level
while viewing the plot if you press the alternate mouse button and
select Pane Options. To test whether the model fits the data
adequately, select Model Comparisons
from the list of Tabular Options.
Model Comparison
----------------
Data variable: Occupancy rate
Number of observations = 84
Start index = 1.97
Sampling interval = 1,0 month(s)
Length of seasonality = 12
Models
------
(A) Simple exponential smoothing with alpha = 0,6976
Seasonal adjustment: Additive
(B) ARIMA(2,0,1)x(2,0,1)12 with constant
(C) ARIMA(3,0,2)x(3,0,2)12 with constant
(D) ARIMA(4,0,3)x(4,0,3)12 with constant
(E) Winter's exp. smoothing
with alpha = 0,3154, beta = 0,0801, gamma = 0,5269
Estimation Period
Model RMSE MAE MAPE ME MPE
------------------------------
(A) 1,82515 1,25441 2,95601 0,098631 0,126659
(B) 1,86524 1,35401 3,16279 0,233571 0,512876
(C) 1,66846 1,20884 2,80188 0,189659 0,393969
(D) 1,1915 0,738663 1,69042 0,17732 0,44224
(E) 2,33067
1,74217 4,13017
-0,281747 -0,730264
Model RMSE RUNS RUNM AUTO MEAN VAR
------------------------------
(A) 1,82515 OK OK * OK OK
(B) 1,86524 OK OK OK OK OK
(C) 1,66846 OK OK OK OK OK
(D) 1,1915 OK OK ** OK ***
(E) 2,33067
OK ** OK OK
OK
Key:
RMSE = Root Mean Squared Error
RUNS = Test for excessive runs up and down
RUNM = Test for excessive runs above and below median
AUTO = Box-Pierce test for excessive autocorrelation
MEAN = Test for difference in mean 1st half to 2nd half
VAR = Test for difference in variance 1st half to 2nd half
OK = not significant (p >= 0.05)
* = marginally significant (0.01 < p <= 0.05)
** = significant (0.001 < p <= 0.01)
*** = highly significant (p
<= 0.001)
The StatAdvisor
---------------
This table compares the results of five different forecasting
models. You can change any of the models by pressing the alternate
mouse button and selecting Analysis Options. Looking at the error
statistics, the model with the smallest root mean squared error (RMSE)
during the estimation period is model D. The model with the smallest
mean absolute error (MAE) is model D. The model with the smallest
mean absolute percentage error (MAPE) is model D. You can use these
results to select the most
appropriate model for your needs.
The table also summarizes the results of five tests run on the
residuals to determine whether each model is adequate for the data.
An OK means that the model passes the test. One * means that it fails
at the 95% confidence level. Two *'s means that it fails at the 99%
confidence level. Three *'s means that it fails at the 99.9%
confidence level. Note that the currently selected model, model C,
passes 5 tests. Since no tests are statistically significant at the
95% or higher confidence level, the current model is probably adequate
for the data.
Estimated Autocorrelations
for residuals
Data variable: Occupancy rate
Model: ARIMA(3,0,2)x(3,0,2)12 with constant
Lag Autocorrelation Stnd. Error Prob. Limit Prob. Limit
------------------------------
1 0,0401693 0,109109 -0,21385 0,21385
2 0,0161815 0,109285 -0,214195 0,214195
3 -0,0353437 0,109313 -0,214251 0,214251
4 -0,070951 0,109449 -0,214517 0,214517
5 -0,0773262 0,109996 -0,215588 0,215588
6 -0,0392128 0,110641 -0,216852 0,216852
7 0,0105494 0,110806 -0,217176 0,217176
8 -0,0555807 0,110818 -0,2172 0,2172
9 -0,0134921 0,111149 -0,217849 0,217849
10 -0,0468478 0,111169 -0,217887 0,217887
11 0,127197 0,111404 -0,218348 0,218348
12 -0,0607652 0,113119 -0,22171 0,22171
13 0,0388762 0,113507 -0,222471 0,222471
14 0,0391377 0,113666 -0,222781 0,222781
15 0,01551 0,113826 -0,223095 0,223095
16 -0,037441 0,113851 -0,223145 0,223145
17 -0,133848 0,113998 -0,223432 0,223432
18 -0,137068 0,115853 -0,227069 0,227069
19 0,0557394 0,117768 -0,230822 0,230822
20 0,146844 0,118082 -0,231437 0,231437
21 0,0099244 0,120236 -0,235659 0,235659
Информация о работе Прогнозирование в индустрии гостеприимства и туризма