Wavelets in Multi-Scale Time Series Analysis: An Application to Seismic Data

Abstract

Aims and ScopeOptimization has continued to expand in all directions at an astonishing rate. New algorithmic and theoretical techniques are continually developing and the diffusion into other disciplines is proceeding at a rapid pace, with a spot light on machine learning, artificial intelligence, and quantum computing. Our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in areas not limited to applied mathematics, engineering, medicine, economics, computer science, operations research, and other sciences.The series Springer Optimization and Its Applications (SOIA) aims to publish state-of-the-art expository works (monographs, contributed volumes, textbooks, handbooks) that focus on theory, methods, and applications of optimization. Topics covered include, but are not limited to, nonlinear optimization, combinatorial optimization, continuous optimization, stochastic optimization, Bayesian optimization, optimal control, discrete optimization, multi-objective optimization, and more. New to the series portfolio include Works at the intersection of optimization and machine learning, artificial intelligence, and quantum computing.Volumes from this series are indexed by Web of Science, zbMATH, Mathematical Reviews, and SCOPUS.

Earthquake pattern analysis using subsequence time series clustering

“…Whereas the second architecture relies on the convolutional long short-term memory (ConvLSTM) and long short-term memory (LSTM) to prevent power shortage and oversupply by doing precise power consumption forecasts. Similarly, time-series analysis are effectively applied in marketing [ 34 ], IoT [ 37 ], seismic signal processing [ 4 ], flood detection [ 3 ] and many diversified applications [ 9 , 29 ].…”