Time Series Algorithms Recipes von Akshay R Kulkarni | Implement Machine Learning and Deep Learning Techniques with Python | ISBN 9781484289785

Time Series Algorithms Recipes

Implement Machine Learning and Deep Learning Techniques with Python

von Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni und V Adithya Krishnan
Mitwirkende
Autor / AutorinAkshay R Kulkarni
Autor / AutorinAdarsha Shivananda
Autor / AutorinAnoosh Kulkarni
Autor / AutorinV Adithya Krishnan
Buchcover Time Series Algorithms Recipes | Akshay R Kulkarni | EAN 9781484289785 | ISBN 1-4842-8978-1 | ISBN 978-1-4842-8978-5

Time Series Algorithms Recipes

Implement Machine Learning and Deep Learning Techniques with Python

von Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni und V Adithya Krishnan
Mitwirkende
Autor / AutorinAkshay R Kulkarni
Autor / AutorinAdarsha Shivananda
Autor / AutorinAnoosh Kulkarni
Autor / AutorinV Adithya Krishnan

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.