Название: Computational Intelligence-based Time Series AnalysisАвтор: Dinesh C. S. Bisht, Mangey Ram
Издательство: River Publishers, Routledge
Год: 2022
Страниц: 191
Язык: английский
Формат: pdf (true)
Размер: 15.6 MB
The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of Machine Learning {ML). There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or are too difficult to model mathematically.
The present study is a real-time application of Artificial Neural Networks (ANNs) to estimate, predict and forecast the suspended sediment transport in streams and river systems using time series data. NN approach is a crucial and readily adaptable important methodology when hydrograph/unit hydrograph methodology and conventional mathematics are inconvenient to apply for an emergency situation to act as the sediment runoff time series data is highly complex in nature i.e. situations like availability of less amount of data, sometimes the very high and huge quantity of data, poor quality of data, erratic nature of data, need of emergency prediction and forecasting, etc.
This book aims to cover the recent advances in time series and applications of CI for time series analysis.
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