Прогнозирование в индустрии гостеприимства и туризма

     Expected number of runs = 40,975

     Large sample test statistic z = 2,14482

     P-value = 0,0319669 

Runs up and down

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

     Number of runs up and down = 65

     Expected number of runs = 55,0

     Large sample test statistic z = 2,50058

     P-value = 0,0123991 

Box-Pierce Test

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

     Test based on first 24 autocorrelations

     Large sample test statistic = 34,231

     P-value = 0,0806472 
 
 

The StatAdvisor

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

   Three tests have been run to determine whether or not adjusted

Occupancy rate is a random sequence of numbers.  A time series of

random numbers is often called white noise, since it contains equal

contributions at many frequencies.  The first test counts the number

of times the sequence was above or below the median.  The number of

such runs equals 51, as compared to an expected value of 40,975 if the

sequence were random.  Since the P-value for this test is less than

0.01, we can reject the hypothesis that the series is random at the

99% confidence level.  The second test counts the number of times the

sequence rose or fell.  The number of such runs equals 65, as compared

to an expected value of 55,0 if the sequence were random.  Since the

P-value for this test is less than 0.01, we can reject the hypothesis

that the series is random at the 99% confidence level.  The third test

is based on the sum of squares of the first 24 autocorrelation

coefficients.  Since the P-value for this test is less than 0.10, we

can reject the hypothesis that the series is random at the 90%

confidence level.  Since the three tests are sensitive to different

types of departures from random behavior, failure to pass any test

suggests that the time series may not be completely random. 
 
 

Automatic Forecasting - Occupancy rate 
 

Seasonal Decomposition - Occupancy rate 

Analysis Summary 

Data variable: Occupancy rate 

Number of observations = 84

Start index =  1.97          

Sampling interval = 1,0 month(s)

Length of seasonality = 12 

Seasonal Decomposition

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

Method: Multiplicative 
 

The StatAdvisor

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

   This procedure applies a multiplicative seasonal decomposition to

Occupancy rate.  The purpose of the decomposition is to separate

Occupancy rate into trend-cycle, seasonal, and random components.  The

data cover 84 time periods.  Each of the tables and graphs shows

different aspects of the decomposition.   
 
 

 

Data Table for Occupancy rate 

Seasonal decomposition method: Multiplicative 

                                                            Seasonally

Period      Data        Trend-Cycle Seasonality Irregular   Adjusted

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

1.97       29,0                                            41,0049    

2.97       36,0                                            40,7078    

3.97       38,0                                            42,2875    

4.97       39,0                                            39,8371    

5.97       45,0                                            39,9569    

6.97       49,0                                            40,8839    

7.97       40,0        42,25       94,6746     89,5224     37,8232    

8.97       55,0        42,4583     129,539     102,651     43,5839    

9.97       53,0        42,5833     124,462     102,939     43,835     

10.97       47,0        42,625      110,264     107,017     45,6159    

11.97       42,0        42,7083     98,3415     105,722     45,1519    

12.97       33,0        42,5833     77,4951     108,084     46,0258    

1.98       31,0        42,5417     72,8697     103,035     43,8329    

2.98       39,0        42,5417     91,6748     103,663     44,1002    

3.98       38,0        42,25       89,9408     100,089     42,2875    

4.98       40,0        41,875      95,5224     97,5726     40,8585    

5.98       46,0        41,5        110,843     98,4212     40,8448    

6.98       45,0        41,1667     109,312     91,206      37,5465    

7.98       43,0        40,9583     104,985     99,2715     40,6599    

8.98       52,0        40,75       127,607     101,12      41,2066    

9.98       49,0        40,5417     120,863     99,963      40,5267    

10.98       42,0        40,5417     103,597     100,546     40,7632    

11.98       38,0        40,625      93,5385     100,558     40,8517    

12.98       29,0        40,9583     70,8037     98,7514     40,4469    

1.99       30,0        41,4583     72,3618     102,317     42,4189    

2.99       35,0        41,75       83,8323     94,7954     39,5771    

3.99       37,0        42,0417     88,0079     97,9378     41,1747    

4.99       41,0        42,4167     96,6601     98,7348     41,88      

5.99       47,0        42,75       109,942     97,6204     41,7327    

6.99       52,0        42,9583     121,048     100,998     43,387     

7.99       48,0        42,9167     111,845     105,758     45,3878    

8.99       54,0        42,875      125,948     99,8051     42,7915    

9.99       54,0        42,9167     125,825     104,067     44,662     

10.99       46,0        43,0833     106,77      103,626     44,6454    

11.99       42,0        43,375      96,83       104,097     45,1519    

12.99       30,0        43,5        68,9655     96,1877     41,8417    

1.00       28,0        43,4167     64,4914     91,1885     39,591     

2.00       36,0        43,25       83,237      94,1222     40,7078    

3.00       37,0        43,0417     85,9632     95,6624     41,1747    

4.00       45,0        42,7083     105,366     107,627     45,9659    

5.00       50,0        42,3333     118,11      104,874     44,3965    

6.00       52,0        42,2083     123,198     102,793     43,387     

7.00       46,0        42,3333     108,661     102,748     43,4967    

8.00       52,0        42,5        122,353     96,9567     41,2066    

9.00       51,0        42,625      119,648     98,9579     42,1808    

10.00       41,0        42,4167     96,6601     93,8136     39,7926    

11.00       38,0        42,0        90,4762     97,266      40,8517    

12.00       31,0        41,875      74,0299     103,251     43,2364    

1.01       30,0        41,875      71,6418     101,299     42,4189    

2.01       38,0        41,9583     90,566      102,41      42,9694    

3.01       38,0        42,0417     90,3865     100,585     42,2875    

4.01       39,0        42,0833     92,6733     94,6624     39,8371    

5.01       46,0        42,2083     108,983     96,7695     40,8448    

6.01       53,0        42,25       125,444     104,666     44,2214    

7.01       45,0        42,2917     106,404     100,613     42,5511    

8.01       55,0        42,375      129,794     102,853     43,5839    

9.01       50,0        42,5        117,647     97,3029     41,3537    

10.01       43,0        42,7083     100,683     97,7179     41,7337    

11.01       39,0        43,0        90,6977     97,5041     41,9268    

12.01       31,0        43,0833     71,9536     100,355     43,2364    

1.02       31,0        43,0417     72,0232     101,838     43,8329    

2.02       39,0        42,9583     90,7856     102,658     44,1002    

3.02       40,0        42,8333     93,3852     103,922     44,5132    

4.02       42,0        42,8333     98,0545     100,159     42,9015    

5.02       50,0        42,7917     116,845     103,75      44,3965    

6.02       51,0        42,6667     119,531     99,7328     42,5527    

7.02       46,0        42,5833     108,023     102,145     43,4967    

8.02       52,0        42,625      121,994     96,6723     41,2066    

9.02       50,0        42,7083     117,073     96,8283     41,3537    

10.02       43,0        42,875      100,292     97,3381     41,7337    

11.02       38,0        43,0417     88,2865     94,912      40,8517    

12.02       29,0        43,2917     66,9875     93,4289     40,4469    

1.03       31,0        43,6667     70,9924     100,381     43,8329    

2.03       40,0        44,1667     90,566      102,41      45,2309    

3.03       41,0        44,7917     91,5349     101,863     45,626     

4.03       45,0        45,375      99,1736     101,302     45,9659    

5.03       51,0        45,9167     111,071     98,6231     45,2845    

6.03       56,0        46,4167     120,646     100,663     46,7245    

7.03       50,0                                            47,279     

8.03       60,0                                            47,5461    

9.03       57,0                                            47,1433    

10.03       50,0                                            48,5276    

11.03       44,0                                            47,302     

12.03       35,0                                            48,8153      
 

The StatAdvisor

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

   This table shows each step of the seasonal decomposition.  The

trend-cycle column shows the results of a centered moving average of

length 12 applied to Occupancy rate.  The seasonality column shows the

data divided by the moving average and multiplied by 100.  Seasonal

indices are then computed for each month by averaging the ratios

across all observations in that month, and scaling the indices so that

an average month equals 100.  The data is then divided by the

trend-cycle and seasonal estimates to give the irregular or residual

component.  This component is then multiplied by 100.   
 
 

 

Seasonal Indices for Occupancy rate 

Seasonal decomposition method: Multiplicative 

Season      Index

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

1           70,7232    

2           88,435     

3           89,861     

4           97,8988    

5           112,621    

6           119,852    

7           105,755    

8           126,193    

9           120,908    

10          103,034    

11          93,0193    

12          71,6989      
 

The StatAdvisor

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

   This table shows the seasonal indices for each month, scaled so

that an average month equals 100.  The indices range from a low of

70,7232 in month 1 to a high of 126,193 in month 8.  This indicates

that there is a seasonal swing from 70,7232% of average to 126,193% of

average throughout the course of one complete cycle. 
 
 

 

 

 

 
 
 
 

Analysis Summary 

Data variable: Occupancy rate 

Number of observations = 84

Start index =  1.97          

Sampling interval = 1,0 month(s)

Length of seasonality = 12 

Forecast Summary

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

Seasonal adjustment: Multiplicative 

Forecast model selected: Simple exponential smoothing with alpha = 0,7043

Number of forecasts generated: 24

Number of periods withheld for validation: 0