| 书目名称 | Time Series Algorithms Recipes | | 副标题 | Implement Machine Le | | 编辑 | Akshay R Kulkarni,Adarsha Shivananda,V Adithya Kri | | 视频video | | | 概述 | Teaches the implementation of various concepts for time-series analysis and modeling with Python.Covers univariate and multivariate modeling using open source packages like Fbprohet, stats model, and | | 图书封面 |  | | 描述 | 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 (autoreg | | 出版日期 | Book 2023 | | 关键词 | Time Series; Python; Data Science; Univariate; Multivariate; Machine Learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-8978-5 | | isbn_softcover | 978-1-4842-8977-8 | | isbn_ebook | 978-1-4842-8978-5 | | copyright | Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan 2023 |
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发表于 2025-3-21 19:51:35
