Several signal processing tools are integrated into machine learning models for performance and computational cost improvements. Fourier transform (FT) its variants, which powerful spectral analysis, employed in the prediction of univariate time series by converting them to sequences domain be processed further recurrent neural networks (RNNs). This approach increases reduces training compared ...

We survey aspects of prediction theory in infinitely many dimensions, with a view to the and applications functional time series.

This paper proposes a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs). While identifying FCs is pivotal to alleviating failures, complex spatio-temporal dependencies among components power system render challenges modeling and analyzing FCs. Furthermore, faces an inherent combinatorial complexity that grows exponentially with size system. The propo...

In today’s day and age, a mobile phone has become basic requirement needed for anyone to thrive. With the cellular traffic demand increasing so dramatically, it is now necessary accurately predict user in networks, improve performance terms of resource allocation utilization. Since learning prediction classical appealing field, which still yields many meaningful results, there been an interest ...

Statistical techniques have disadvantages in handling the non-linear pattern. Soft computing (SC) techniques such as artificial neural networks are considered to be better for prediction of data with non-linear patterns. In the real-life, timeseries data comprise complex pattern, and hence it may be difficult to obtain high prediction accuracy rates using the statistical or SC techniques indivi...

Kernel regression or classification (also referred to as weighted -NN methods in Machine Learning) are appealing for their simplicity and therefore ubiquitous in data analysis. However, practical implementations of kernel regression or classification consist of quantizing or sub-sampling data for improving time efficiency, often at the cost of prediction quality. While such tradeoffs are necess...

Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction's accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several m...