Deep Learning for Time Series Classification

Abstract

Time Series Classification (TSC) has been measured as the most demanding crisis in data mining. With the augment of temporal statistics accessibility, thousands of numerical algorithms have been planned since 2016 because of their ordinary temporal ordering; time series data can be found in all kinds of tasks that require some kind of human thinking process. In the recent past, time series categorization became one of the most important problems to be tackled because of various information-keeping categorization problems. Time series can be found in several applications of the present world from the health sector, to personal activity, and detection of anomalies. As temporal data has been made accessible in recent years many new zones are becoming very interested in time series applications and based on that newer numerical chain algorithms have been suggested. Many machines today can be performed without actually touching them and to serve this purpose this machine store a series of pictures that can analyze the user signals. Recognizing the correct signal from the set of pictures can be a time series classification problem. © 2024 IEEE.