When dealing with problems that require checking the answer of some ranges inside a given array, the sliding window algorithm can be a very powerful technique. In this tutorial, we’ll explain the sliding window technique with both its variants, the fixed and flexible window sizes. Also, we’...
While presenting the data stream model in Chapter 1, we explained it using two examples. In one example, the input for the algorithm appeared on a magnetic tape which can be efficiently accessed only in a sequential manner. In this example, the reason that we got the input in the form ...
On the other hand, increasing c also has a positive impact on the accuracy of the method, since, the larger c is, the more our algorithm approaches the ideal algorithm explained in the previous subsection. However, larger values of c increase both the memory and CPU requirements of our ...
Algorithm 1 performs the sliding window exponentiation method, assuming that the exponent is given in a windowed form, in two steps: It first precomputes the values of\(b^1 \bmod p, b^3 \bmod p, \cdots , b^{2^w-1} \bmod p\)for odd powers ofb. Then, the algorithm scans the...
2.1. Computation of SSTD window-sizes 2.1.1. EMD decomposition EMD uses a sifting algorithm to decompose the original multicomponent time series into nearly orthogonal modes spanning narrow frequency bands (Patrick and Goncalves, 2004), which are named intrinsic mode functions (IMFs). Let y(t)∈...
We used LR as estimation algorithm and used the entire CPU utilization data of the selected VM as a time series. We identified the optimal observation window size using exhaustive search among window sizes 2 to 30 and chosen the window size which yields maximum accuracy as the optimal window ...
Calculations show that the operations required are partially redundant and a computation algorithm can be found which minimizes the number of required operations. This algorithm makes it possible to obtain the term Xkm+1 from the sample xmof the signal and from the previously computed term (Xkm)...
Fig. 6. Epicentral map of the earthquakes listed in Table 1, located using a 3D velocity model with SIMULPS algorithm. (a) Distribution of the seismic and GNSS stations in the Etna region; (b) Fault plane solution of the ML 3.6, 2009 mainshock. In about a month, twenty-five earthquake...
The fundamental parameters of the method are explained below: Figure 1. Representation of the sliding window method. Position of the window, which is identified according to the position of its center, which, taking into account that the rows will be identified as i, and the columns as j...
Similarly, the ARIMA model with the confidence interval was employed with the moving window in [25]. Various machine learning models were extensively implemented for the anomaly detection, such as a density-based approach [26], SVM-based models [27,28], and the isolation forest algorithm [29,...