Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python

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 For

Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.