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. It begins with the fundamentals of time series forecasting using statistical
modeling methods like AR (autoregressive) MA (moving-average) ARMA (autoregressive
moving-average) and ARIMA (autoregressive integrated moving-average). Next you'll learn
univariate and multivariate modeling using different open-sourced packages like Fbprohet stats
model and sklearn. You'll 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. After finishing
this book you will have a foundational understanding of various concepts relating to time
series and its implementation in Python. What You Will Learn Implement various techniques in
time series analysis using Python. Utilize statistical modeling methods such as AR
(autoregressive) MA (moving-average) ARMA (autoregressive moving-average) and ARIMA
(autoregressive integrated moving-average) for time series forecasting Understand univariate
and multivariate modeling for time series forecasting Forecast using machine learning and deep
learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData
Scientists Machine Learning Engineers and software developers interested in time series
analysis.
