Прогнозирование в индустрии гостеприимства и туризма

Автор работы: Пользователь скрыл имя, 08 Декабря 2011 в 00:01, курсовая работа

Описание работы

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.

Содержание работы

Введение…………………………………………………………….……………3



I. Теоретическое обоснование прогнозирования в индустрии гостеприимства и туризма
Сущность и методы прогнозирования…………………………….…….….5

Понятие временных рядов и основные этапы их анализа……………....…7

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……………

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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            

------------------------------------------------------------------------------ 

                                     Lower 95,0%         Upper 95,0%        

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

                                              Lower 95,0%       Upper 95,0%      

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         

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