In this Book, different normalization methods are used on time series data before feeding the data into the DNN model and we try to find out the impact of each normalization technique on DNN for TSF.

Raghurami Reddy Etukuru, Ph.D., a distinguished and adaptable specialist in data science and artificial intelligence, delves into that question in this groundbreaking book, explaining that the factors are numerous and multifaceted, each ...

Deep learning has recently risen as a dominant technique in a variety of settings comprising large-scale and high-dimensional data.

The forecasting of financial time series has been very important in both finance industry and academia for several years due to the volatile and unstable nature of financial systems.

... literature review shows that ML methods play crucial roles in the domain of forecasting time series data. The use of ... Tutorial Survey of Architectures, Algorithms, and Application for Deep Learning-ERRATUM”. APSIPA Transaction ...

In recent years many new algorithms have been developed for applications in speech and image processing which may be repurposed for time series prediction.

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications.

Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics.