Climate Time Series Analysis: Classical Statistical And

This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It contains many examples in different areas of time series analysis such as autoregressive modelling and spectral analysis, linear extrapolation, simulation, causality, relations between scalar components of multivariate time series, and reconstructions of climate data.

This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

Technical Analysis (SMA and RSI), Time Series Analysis (ARIMA and GRACH), Machine Learning