Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
暫譯: 時間序列演算法食譜：使用 Python 實現機器學習與深度學習技術

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

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

商品描述

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 (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.

商品描述(中文翻譯)

本書透過問題解決式的食譜，教導如何使用 Python 實作各種時間序列分析與建模的概念，從數據讀取和預處理開始。
本書首先介紹使用統計建模方法進行時間序列預測的基本概念，包括自回歸 (AR)、移動平均 (MA)、自回歸移動平均 (ARMA) 和自回歸整合移動平均 (ARIMA)。接下來，您將學習使用不同的開源套件，如 Fbprophet、statsmodel 和 sklearn 進行單變量和多變量建模。您還將深入了解基於經典機器學習的回歸模型，如 randomForest、Xgboost 和 LightGBM，以解決預測問題。本書最後展示了如何實作深度學習模型（LSTM 和 ANN）進行時間序列預測。每一章都包含多個程式碼範例和插圖。完成本書後，您將對與時間序列相關的各種概念及其在 Python 中的實作有基本的理解。
您將學到什麼


  • 使用 Python 實作各種時間序列分析技術。

  • 利用統計建模方法，如自回歸 (AR)、移動平均 (MA)、自回歸移動平均 (ARMA) 和自回歸整合移動平均 (ARIMA) 進行時間序列預測。

  • 理解時間序列預測的單變量和多變量建模。

  • 使用機器學習和深度學習技術（如 GBM 和 LSTM）進行預測。


本書適合誰閱讀
對時間序列分析感興趣的數據科學家、機器學習工程師和軟體開發人員。

作者簡介

Akshay Kulkarni is an AI and machine learning (ML) evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He has been honoured as Google Developer Expert, and is an Author and a regular speaker at top AI and data science conferences (including Strata, O'Reilly AI Conf, and GIDS). He is a visiting faculty member for some of the top graduate institutes in India. In 2019, he has been also featured as one of the top 40 under 40 Data Scientists in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is a Data science and MLOps Leader. He is working on creating worldclass MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.

Anoosh Kulkarni is a data scientist and a Senior AI consultant. He has worked with global clients across multiple domains and helped them solve their business problems using machine learning (ML), natural language processing (NLP), and deep learning.. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family.

V Adithya Krishnan is a data scientist and ML Ops Engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced Machine learning (ML) applications. He has experience across multiple fields of AI-ML, including, Time-series forecasting, Deep Learning, NLP, ML Operations, Image processing, and data analytics. Presently, he is working on a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He also published a paper at an IEEE conference, "Deep Learning Based Approach for Range Estimation," written in collaboration with the DRDO. He lives in Chennai with his family.


作者簡介(中文翻譯)

**阿克沙伊·庫爾卡尼** 是一位人工智慧（AI）和機器學習（ML）的推廣者及思想領袖。他曾為多家《財富》500 強企業及全球企業提供諮詢，推動以 AI 和數據科學為主導的戰略轉型。他被授予 Google 開發者專家稱號，並且是多個頂尖 AI 和數據科學會議（包括 Strata、O'Reilly AI Conf 和 GIDS）的作者及常規演講者。他是印度一些頂尖研究所的客座教授。2019 年，他被評選為印度 40 位以下的頂尖數據科學家之一。在空閒時間，他喜歡閱讀、寫作、編程，並幫助有志於成為數據科學家的朋友。他與家人居住在班加羅爾。

**阿達爾沙·希瓦南達** 是一位數據科學和 MLOps 領導者。他致力於創建世界級的 MLOps 能力，以確保從 AI 中持續交付價值。他的目標是在組織內外建立一支卓越的數據科學家團隊，通過培訓計劃解決問題，並始終希望走在潮流的前端。他在製藥、醫療保健、消費品、零售和市場營銷領域有著廣泛的工作經驗。他居住在班加羅爾，熱愛閱讀和教授數據科學。

**阿努什·庫爾卡尼** 是一位數據科學家和高級 AI 顧問。他曾與多個領域的全球客戶合作，幫助他們利用機器學習（ML）、自然語言處理（NLP）和深度學習解決商業問題。阿努什熱衷於指導和輔導人們在數據科學的旅程中。他主導數據科學/機器學習的聚會，幫助有志於成為數據科學家的朋友規劃職業生涯。他還在大學舉辦 ML/AI 工作坊，並積極參與舉辦有關 AI 和數據科學的網絡研討會、演講和會議。他與家人居住在班加羅爾。

**V·阿迪提亞·克里希南** 是一位數據科學家和 ML Ops 工程師。他曾與多個領域的全球客戶合作，廣泛利用先進的機器學習（ML）應用幫助他們解決商業問題。他在 AI-ML 的多個領域擁有經驗，包括時間序列預測、深度學習、NLP、ML 操作、圖像處理和數據分析。目前，他正在為生產中的模型開發一套最先進的價值可觀察性套件，該套件包括持續的模型和數據監控以及實現的商業價值。他還在 IEEE 會議上發表了一篇論文，題為《基於深度學習的範圍估計方法》，該論文是與 DRDO 合作撰寫的。他與家人居住在金奈。

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