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

This book teaches the practical implementation of various concepts for series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing.

It bs with the fundamentals of 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 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 series and its implementation in Python.

What You Will Learn

Implement various techniques in 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 series forecasting

Understand univariate and multivariate modeling for 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 Eeers, and software developers interested in series analysis.