Sentence examples for wind sequence from inspiring English sources

Parameters in Table 1 are the foundations of solving probability distribution of each wind sequence.

The centre of each density maximum corresponds in the smoothed matrix to the most representative wind sequence for the 5 days previous to that LC6 class.

In fact they were initially their image vectors (close to the observed sequences) but then were averaged by smoothing and later approximated to integer values to the closest observed wind sequence.

Now, as an example, let us take from Table 5 the most probable wind sequence, from the fifth to the first day before the target day with AN: A SW CSW C NE.

It is also important to note that the wind sequences of Table 5 are not necessarily observed sequences in 1975 94.

In any case, Table 5 gives the most probable wind sequences previous to a given LC, considering the 1975 94 period.

The generalization is possible because the wind sequences of the test periods are enough represented in the training period.

We confirmed this by supervising the 5BLC's wind sequences of 1992_93 with the non correlated winds of the 36th day ahead, BLC36.

It gives, as a first result, the most probable wind sequences preceding each one of the 27 CWT Lamb classes in that period.

Assume we want to classify by similarity all the 726 (365+366−5) 5BLC(k) 15D vectors for all the 5 days wind sequences corresponding to the two year period 1992 93.

However, although the observed wind persistence is clearly conditioning the average prediction, the data mining by KNN allows individual insight into the wind sequences distribution and the particular wind prediction from the previous wind sequences, beyond the average results, which can be clearly seen in Tables 5 and 6.