Forecasting

Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python

This book is an illustration of the use of mathematics to solve specific problems in engineering, statistics, and science in general.

This book comprises the proceedings of the 26th International Conference on Hydraulics, Water Resources and Coastal Engineering (HYDRO 2021) focusing on broad spectrum of emerging opportunities and challenges in the field of flood forecasting and hydraulic structures. It covers a range of topics, including, but not limited to, early warning system, urban flood modelling and management, dam hazard classification, river training and protection works, structural and non-structural measures for flood mitigation, assessment and development of flood vulnerability, hazard and risk maps rehabilitation of old dams, streamflow turbines, canal operation and related structure, operation and management of dams including their instrumentation etc.

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time.