Прогнозирование в индустрии гостеприимства и туризма

22        -0,0574692        0,120246          -0,235678         0,235678         

23        0,0285018         0,120572          -0,236318         0,236318         

24        0,0601561         0,120653          -0,236475         0,236475           
 

The StatAdvisor

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

   This table shows the estimated autocorrelations between the

residuals at various lags.  The lag k autocorrelation coefficient

measures the correlation between the residuals at time t and time t-k.

Also shown are 95,0% probability limits around 0.0.  If the

probability limits at a particular lag do not contain the estimated

coefficient, there is a statistically significant correlation at that

lag at the 95,0% confidence level.  In this case, none of the 24

autocorrelations coefficients are statistically significant, implying

that the time series may well be completely random (white noise).  You

can plot the autocorrelation coefficients by selecting Residual

Autocorrelation Function from the list of Graphical Options. 
 
 

 

Periodogram for residuals 

Data variable: Occupancy rate

Model: ARIMA(3,0,2)x(3,0,2)12 with constant

                                                Cumulative      Integrated     

Frequency       Period          Ordinate        Sum             Periodogram    

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

0,0                             7,69327E-29     7,69327E-29     3,84292E-31    

0,0119048       84,0            3,77987         3,77987         0,0188811      

0,0238095       42,0            5,47069         9,25056         0,0462081      

0,0357143       28,0            2,48865         11,7392         0,0586393      

0,047619        21,0            5,3781          17,1173         0,0855039      

0,0595238       16,8            6,51659         23,6339         0,118055       

0,0714286       14,0            5,87847         29,5124         0,147419       

0,0833333       12,0            5,36404         34,8764         0,174214       

0,0952381       10,5            14,723          49,5995         0,247758       

0,107143        9,33333         0,288574        49,888          0,249199       

0,119048        8,4             3,7121          53,6001         0,267742       

0,130952        7,63636         5,94088         59,541          0,297417       

0,142857        7,0             3,11662         62,6576         0,312985       

0,154762        6,46154         14,5929         77,2505         0,385879       

0,166667        6,0             2,35442         79,6049         0,39764        

0,178571        5,6             1,41688         81,0218         0,404717       

0,190476        5,25            2,06301         83,0848         0,415023       

0,202381        4,94118         13,9485         97,0333         0,484698       

0,214286        4,66667         0,540237        97,5735         0,487396       

0,22619         4,42105         0,398674        97,9722         0,489388       

0,238095        4,2             0,5232          98,4954         0,492001       

0,25            4,0             5,5666          104,062         0,519807       

0,261905        3,81818         11,2391         115,301         0,575948       

0,27381         3,65217         1,30725         116,608         0,582478       

0,285714        3,5             3,97918         120,588         0,602355       

0,297619        3,36            5,16561         125,753         0,628158       

0,309524        3,23077         6,27203         132,025         0,659488       

0,321429        3,11111         3,30586         135,331         0,676001       

0,333333        3,0             0,653559        135,985         0,679266       

0,345238        2,89655         7,57787         143,562         0,717119       

0,357143        2,8             5,59333         149,156         0,745058       

0,369048        2,70968         2,87681         152,033         0,759429       

0,380952        2,625           8,00965         160,042         0,799438       

0,392857        2,54545         0,456361        160,499         0,801718       

0,404762        2,47059         7,37219         167,871         0,838543       

0,416667        2,4             0,77842         168,649         0,842431       

0,428571        2,33333         8,552           177,201         0,88515        

0,440476        2,27027         3,93841         181,14          0,904823       

0,452381        2,21053         6,49727         187,637         0,937278       

0,464286        2,15385         1,73383         189,371         0,945939       

0,47619         2,1             5,05297         194,424         0,971179       

0,488095        2,04878         4,82504         199,249         0,995281       

0,5             2,0             0,944683        200,193         1,0              
 

The StatAdvisor

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

   This table shows the periodogram ordinates for the residuals.  It

is often used to identify cycles of fixed frequency in the data.  The

periodogram is constructed by fitting a series of sine functions at

each of 43 frequencies.  The ordinates are equal to the squared

amplitudes of the sine functions.  The periodogram can be thought of

as an analysis of variance by frequency, since the sum of the

ordinates equals the total corrected sum of squares in an ANOVA table.

You can plot the periodogram ordinates by selecting Periodogram from

the list of Graphical Options. 
 
 

 

Tests for Randomness of residuals 

Data variable: Occupancy rate

Model: ARIMA(3,0,2)x(3,0,2)12 with constant

Runs above and below median

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

     Median = 0,27267

     Number of runs above and below median = 43

     Expected number of runs = 43,0

     Large sample test statistic z = -0,109772

     P-value = 1,08742 

Runs up and down

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

     Number of runs up and down = 56

     Expected number of runs = 55,6667

     Large sample test statistic z = 0,0

     P-value = 1,0 

Box-Pierce Test

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

     Test based on first 24 autocorrelations

     Large sample test statistic = 9,66127

     P-value = 0,786506 
 
 

The StatAdvisor

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

   Three tests have been run to determine whether or not the residuals

form a random sequence of numbers.  A sequence 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 43, as compared to an expected value of 43,0 if the sequence

were random.  Since the P-value for this test is greater than or equal

to 0.10, we cannot reject the hypothesis that the residuals are random

at the 90% or higher confidence level.  The second test counts the

number of times the sequence rose or fell.  The number of such runs

equals 56, as compared to an expected value of 55,6667 if the sequence

were random.  Since the P-value for this test is greater than or equal

to 0.10, we cannot reject the hypothesis that the series is random at

the 90% or higher 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 greater than or equal to 0.10, we cannot

reject the hypothesis that the series is random at the 90% or higher

confidence level.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Приложение 2

 

Средняя загрузка гостиниц Северной Ирландии за 1997—2004 гг.

(%)