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Time Series Algorithms Recipes

Implementation Machine Learning and Deep Learning Techniques with Python

Produktform: Buch / Einband - flex.(Paperback)

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

Sprache(n): Englisch

ISBN: 978-1-4842-8977-8 / 978-1484289778 / 9781484289778

Verlag: APRESS

Erscheinungsdatum: 24.12.2022

Seiten: 174

Auflage: 1

Autor(en): Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan, Akshay R Kulkarni

37,44 € inkl. MwSt.
kostenloser Versand

lieferbar - Lieferzeit 10-15 Werktage

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