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

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

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

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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)

本書通過問題解決式的配方，教授使用Python實現時間序列分析和建模的實際應用。從數據讀取和預處理開始，介紹了時間序列預測的基礎知識，包括統計建模方法，如AR（自回歸）、MA（移動平均）、ARMA（自回歸移動平均）和ARIMA（自回歸差分移動平均）。接下來，您將學習使用不同的開源包（如Fbprohet、stats model和sklearn）進行單變量和多變量建模。您還將瞭解使用隨機森林、Xgboost和LightGBM等經典的基於機器學習的回歸模型進行預測問題。本書最後展示了使用深度學習模型（LSTM和ANN）進行時間序列預測的實現。每章都包含多個代碼示例和插圖。閱讀完本書後，您將對時間序列的各種概念及其在Python中的實現有基礎的理解。

您將學到以下內容：
- 使用Python實現時間序列分析的各種技術。
- 使用統計建模方法（如AR、MA、ARMA和ARIMA）進行時間序列預測。
- 瞭解單變量和多變量建模進行時間序列預測。
- 使用機器學習和深度學習技術（如GBM和LSTM）進行預測。

本書適合數據科學家、機器學習工程師和軟件開發人員對時間序列分析感興趣的讀者。

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)

Akshay Kulkarni是一位AI和機器學習（ML）的倡導者和思想領袖。他曾為多家財富500強和全球企業提供咨詢服務，推動AI和數據科學引領的戰略轉型。他被譽為Google開發者專家，是一位作家，也是頂級AI和數據科學會議（包括Strata、O'Reilly AI Conf和GIDS）的常客演講嘉賓。他是印度一些頂尖研究所的客座教師。2019年，他也被選為印度40位40歲以下的頂尖數據科學家之一。在閒暇時間，他喜歡閱讀、寫作、編程和幫助有志成為數據科學家的人。他與家人一起居住在班加羅爾。

Adarsha Shivananda是一位數據科學和MLOps領導者。他致力於創建世界一流的MLOps能力，以確保從AI中持續提供價值。他的目標是在組織內外建立一支優秀的數據科學家團隊，通過培訓計劃解決問題，並始終保持領先。他在製藥、醫療保健、消費品、零售和營銷領域有豐富的工作經驗。他居住在班加羅爾，喜歡閱讀和教授數據科學。

Anoosh Kulkarni是一位數據科學家和高級AI顧問。他與多個領域的全球客戶合作，利用機器學習（ML）、自然語言處理（NLP）和深度學習幫助他們解決業務問題。Anoosh熱衷於指導和指導人們在數據科學之旅中的成長。他領導數據科學/機器學習聚會，幫助有志成為數據科學家的人規劃職業生涯。他還在大學進行機器學習/人工智能研討會，積極參與AI和數據科學的網絡研討會、講座和活動。他與家人一起居住在班加羅爾。

V Adithya Krishnan是一位數據科學家和ML Ops工程師。他曾與多個全球客戶合作，跨多個領域廣泛應用先進的機器學習（ML）應用解決他們的業務問題。他在AI-ML的多個領域都有經驗，包括時間序列預測、深度學習、NLP、ML運營、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先進的價值可觀察性套件，其中包括連續的模型和數據監控以及實現的業務價值。他還與DRDO合作在IEEE會議上發表了一篇名為“基於深度學習的範圍估計方法”的論文。他與家人一起居住在金奈。

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