Hands-On Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
暫譯: Python 實戰時間序列分析：從基礎到前沿技術

Vishwas, B. V., Patel, Ashish

Hands-On Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
  • 出版商: Apress
  • 出版日期: 2020-08-25
  • 售價: $1,810
  • 貴賓價: 9.5 折 $1,720
  • 語言: 英文
  • 頁數: 407
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484259912
  • ISBN-13: 9781484259917
  • 相關分類: Python、程式語言

    • 海外代購書籍(需單獨結帳)

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商品描述

Learn the concepts of time series from traditional to leading-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.
You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima.

The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more.
What You'll Learn

  • Understand the basics and advanced concepts of time series
  • Design, develop, train, and validate time-series methodologies
  • Review smoothing, ARMA, ARIMA, SARIMA, SRIMAX, VAR, VARMA techniques in time series
  • Leverage bleeding-edge techniques such as ANN, CNN, RNN
  • Work with LSTM, GRU, Autoencoder and fbprophet

Who This Book Is For
Data scientists, data analysts, financial analysts, and stock market researchers

商品描述(中文翻譯)

學習時間序列的概念，從傳統技術到前沿技術。本書使用全面的範例清楚地說明統計方法和分析時間序列數據的技術，以及其在現實世界中的應用。所有的程式碼都可以在 Jupyter notebooks 中獲得。

您將首先回顧時間序列的基本概念、時間序列數據的結構、預處理，以及如何通過數據整理來構建特徵。接下來，您將學習傳統的時間序列技術，如 ARMA、SARIMAX、VAR 和 VARMA，並使用像 StatsModels 和 pmdarima 這樣的趨勢框架。

本書還解釋了如何使用 sktime 構建分類模型，並涵蓋了基於深度學習的先進技術，如 ANN、CNN、RNN、LSTM、GRU 和自編碼器（Autoencoder），以使用 Tensorflow 解決時間序列問題。最後，解釋了流行的框架 fbprophet 用於建模時間序列分析。在閱讀《Hands-On Time Series Analysis with Python》後，您將能夠在石油和天然氣、機器人技術、製造業、政府、銀行、零售、醫療保健等行業應用這些新技術。

您將學到的內容：
- 理解時間序列的基本和進階概念
- 設計、開發、訓練和驗證時間序列方法
- 回顧時間序列中的平滑、ARMA、ARIMA、SARIMA、SRIMAX、VAR、VARMA 技術
- 利用前沿技術，如 ANN、CNN、RNN
- 使用 LSTM、GRU、自編碼器和 fbprophet

本書適合對象：
數據科學家、數據分析師、金融分析師和股市研究人員

作者簡介

Vishwas B V is a Data Scientist, AI researcher and Sr. AI Consultant, Currently living in Bengaluru(INDIA). His highest qualification is Master of Technology in Software Engineering from Birla Institute of Technology & Science, Pilani, and his primary focus and inspiration is Data Warehousing, Big Data, Data Science (Machine Learning, Deep Learning, Timeseries, Natural Language Processing, Reinforcement Learning, and Operation Research). He has over seven years of IT experience currently working at Infosys as Data Scientist & Sr. AI Consultant. He has also worked on Data Migration, Data Profiling, ETL & ELT, OWB, Python, PL/SQL, Unix Shell Scripting, Azure ML Studio, Azure Cognitive Services, and AWS.

Ashish Patel is a Senior Data Scientist, AI researcher, and AI Consultant with over seven years of experience in the field of AI, Currently living in Ahmedabad(INDIA). He has a Master of Engineering Degree from Gujarat Technological University and his keen interest and ambition to research in the following domains such as (Machine Learning, Deep Learning, Time series, Natural Language Processing, Reinforcement Learning, Audio Analytics, Signal Processing, Sensor Technology, IoT, Computer Vision). He is currently working as Senior Data Scientist for Cynet infotech Pvt Ltd. He has published more than 15 + Research papers in the field of Data Science with Reputed Publications such as IEEE. He holds Rank 3 as a kernel master in Kaggle. Ashish has immense experience working on cross-domain projects involving a wide variety of data, platforms, and technologies

作者簡介(中文翻譯)

Vishwas B V 是一位資料科學家、人工智慧研究員及資深人工智慧顧問，目前居住在班加羅爾（印度）。他的最高學歷為比爾拉科技與科學學院（Birla Institute of Technology & Science, Pilani）的軟體工程碩士學位，他的主要專注領域和靈感來源是資料倉儲、大數據、資料科學（機器學習、深度學習、時間序列、自然語言處理、強化學習及運籌學）。他擁有超過七年的資訊科技經驗，目前在印孚瑟斯（Infosys）擔任資料科學家及資深人工智慧顧問。他還曾參與資料遷移、資料分析、ETL 和 ELT、OWB、Python、PL/SQL、Unix Shell 腳本、Azure ML Studio、Azure 認知服務及 AWS 的相關工作。

Ashish Patel 是一位資深資料科學家、人工智慧研究員及人工智慧顧問，擁有超過七年的人工智慧領域經驗，目前居住在艾哈邁達巴德（印度）。他擁有古吉拉特科技大學的工程碩士學位，並對以下領域有濃厚的興趣和研究抱負：（機器學習、深度學習、時間序列、自然語言處理、強化學習、音訊分析、信號處理、感測器技術、物聯網、計算機視覺）。他目前在Cynet infotech Pvt Ltd擔任資深資料科學家。他在資料科學領域已發表超過15篇研究論文，並在IEEE等知名出版物上發表。他在Kaggle中擁有排名第三的核心大師地位。Ashish在涉及各種資料、平台和技術的跨領域專案中擁有豐富的經驗。

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