The sliding window kernel recursive least squares (SW-KRLS) algorithm is one of the most widely used approach in dealing with nonlinear problems because of its simple structure, low computational complexity and high predictive accuracy. However, as data size increases, the computational efficiency of...
We have attempted more complicated measures such as MSM [52] and TWED [31]. They are very time-consuming because they have at least quadratic time complexity, and neither of them (using the Python implementations from sktime [30]) could complete the run within the 2-day time frame for an...
The strategy has relative low computation complexity and can be implemented on-line with no a-priori process knowledge. Recently, Zhang et al. (2015) have presented a method based on the discrete Fourier transform (DFT) within a probability framework with the objective of distinguishing between ...
Therefore, no additional levels of complexity are justified when a fast response is needed, as is the case in real-time decision support systems. Advantages include no additional effort or discomfort for the patient caused by wearing additional sensors and tracking events. To the best of our ...
1. the algorithm of constructing network from distance matrix. 2. evolution of sliding time window 3. the later processing or visual analysis of generated graphs. Thinking: 1.What's the ground truth in load profiles? For clustering, there's no ground truth, so how to tune the parameters or...
Techniques such as deconvolutional networks48 make it possible to map the learned space of a deep learning algorithm back onto the original temporal dataset. This allows one to visualise the features that the algorithm is using to make its decision, which could serve as a starting point for an...
The above touches upon the striking complexity of functional brain activation during a task (Simony et al., 2016; Bolton et al., 2018b; Gonzalez-Castillo and Bandettini, 2018): in this, although standard PPI analysis already provides valuable insight into brain function at the cross-condition ...
Unakafova, V.A., Keller, K., 2013. Efficiently measuring complexity on the basis of real-world data. Entropy, 15(10), 4392-4415. Unakafova, Valentina (2015). Fast permutation entropy (www.mathworks.com/matlabcentral/fileexchange/44161-permutation-entropy--fast-algorithm-), MATLAB Central ...
Furthermore, characteristics such as the variability in performance and the run time complexity of algorithms are also of great interest to the practitioner. We model the approach used in Bagnall et al. (2017) by comparing performance by data characteristics using all 142 datasets. Tables 15, 16...
The concept of “Structural Diversity” of a network refers to the level of dissimilarity between the various agents acting in the system, and it is typically interpreted as the number of connected components in the network. This key property of networks