Time Series Algorithms Recipes

Akshay R Kulkarni, Kulkarni,Adarsha Shivananda, Shivananda,Anoosh Kulkarni, Kulkarni

ISBN 10: 148428979X / ISBN 13: 9781484289792

Verlag: Springer Nature B.V., 2022

Beschreibung

New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Bestandsnummer des Verkäufers L0-9781484289792

Bibliografische Details

• Titel: Time Series Algorithms Recipes
• Verlag: Springer Nature B.V.
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 148428979X
• ISBN-13: 9781484289792
• Einband: PAP
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.