TIME SERIES ALGORITHMS RECIPES IBD

Sinopse

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing.áIt begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressiveá integrated moving-average). Next, youâÇÖll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. YouâÇÖll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations.áAfter finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python.áWhat You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average),á ARMA (autoregressive moving-average) and ARIMA (autoregressiveá integrated moving-average) for time series forecastingáUnderstand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)áWho This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.