Hands-On Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
Vishwas, B. V., Patel, Ashish
買這商品的人也買了...
-
邁向大數據的第一步！R 語言程式設計精要 (R for Everyone: Advanced Analytics and Graphics, 2/e)$450$383 -
$427統計機器學習導論 (Introduction to Statistical Machine Learning) -
Working Effectively with Legacy Code : 管理、修改、重構遺留程式碼的藝術 (中文版)$720$562 -
Python 接班人出世：最新科學專用語言 Julia 入門實戰$690$587 -
Rust 權威指南 (The Rust Programming Language (Covers Rust 2018))$954$906 -
Python 科學計算和數據科學應用 : 使用 NumPy、SciPy 和 matplo, 2/e$1,188$1,129 -
時間序列分析與預測$588$559 -
再強一點：用 Go語言完成六個大型專案 (書況有些許瑕疵,不介意再下單)$780$663 -
Graph Representation Learning (Paperback)$1,980$1,881 -
$1,890Machine Learning for Time Series Forecasting with Python (Paperback) -
Linux Command Line and Shell Scripting Bible, 4/e (Paperback)$1,950$1,853 -
$1,400Network Programming with Go: Learn to Code Secure and Reliable Network Services from Scratch -
Reinforcement Learning｜強化學習深度解析 (繁體中文版) (Reinforcement Learning: An Introduction, 2/e)$1,200$900 -
圖神經網絡導論$419$398 -
Python 資料可視化攻略$480$379 -
$556圖表示學習 -
$1,824Python and R for the Modern Data Scientist: The Best of Both Worlds -
$709業務可視化分析： 從問題到圖形的 Tableau 方法 (全彩) -
黑帽 Python｜給駭客與滲透測試者的 Python 開發指南, 2/e (Black Hat Python : Python Programming for Hackers and Pentesters, 2/e)$450$338 -
Spring REST API 開發與測試指南｜使用 Swagger、HATEOAS、JUnit、Mockito、PowerMock、Spring Test$580$435 -
Linux 網路內功修煉 - 徹底了解底層原理及高性能架構$780$616 -
演算法生存指南$800$632 -
Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning (Paperback)$2,070$1,967 -
Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples$1,870$1,777 -
$301機器學習的算法分析和實踐
相關主題
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
Who This Book Is For
Data scientists, data analysts, financial analysts, and stock market researchers
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)
從傳統到尖端技術，學習時間序列的概念。本書使用詳盡的例子清楚地說明統計方法和分析時間序列數據以及其在現實世界中的應用。所有的程式碼都可以在Jupyter筆記本中找到。您將首先回顧時間序列的基礎知識，時間序列數據的結構，預處理以及如何通過數據整理來製作特徵。接下來，您將使用StatsModels和pmdarima等趨勢框架來查看傳統的時間序列技術，如ARMA、SARIMAX、VAR和VARMA。本書還解釋了使用sktime構建分類模型的方法，並介紹了使用Tensorflow解決時間序列問題的高級深度學習技術，如ANN、CNN、RNN、LSTM、GRU和Autoencoder。最後，本書還解釋了流行的fbprophet框架用於時間序列分析建模。閱讀完《Python時間序列分析實戰》，您將能夠在石油和天然氣、機器人、製造業、政府、銀行、零售、醫療保健等行業應用這些新技術。
您將學到什麼：
- 理解時間序列的基礎和高級概念
- 設計、開發、訓練和驗證時間序列方法
- 回顧平滑、ARMA、ARIMA、SARIMA、SRIMAX、VAR、VARMA等時間序列技術
- 利用ANN、CNN、RNN等尖端技術
- 使用LSTM、GRU、Autoencoder和fbprophet
適合閱讀對象：
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、人工智慧研究員和高級人工智慧顧問，目前居住在印度班加羅爾。他擁有比爾拉理工學院的軟體工程碩士學位，他的主要專注和靈感來源是數據倉儲、大數據、數據科學（機器學習、深度學習、時間序列、自然語言處理、強化學習和運籌學）。他在IT領域擁有超過七年的經驗，目前在Infosys擔任資料科學家和高級人工智慧顧問。他還從事過數據遷移、數據分析、ETL和ELT、OWB、Python、PL/SQL、Unix Shell腳本、Azure ML Studio、Azure認知服務和AWS等工作。
Ashish Patel 是一位高級資料科學家、人工智慧研究員和人工智慧顧問，擁有超過七年的人工智慧領域經驗，目前居住在印度艾哈邁德巴德。他擁有古吉拉特理工大學的工程碩士學位，對機器學習、深度學習、時間序列、自然語言處理、強化學習、音頻分析、信號處理、感測器技術、物聯網和計算機視覺等領域有濃厚的興趣和研究抱負。他目前在Cynet infotech Pvt Ltd擔任高級資料科學家。他在數據科學領域發表了15多篇研究論文，其中包括IEEE等知名出版物。他在Kaggle上擁有核心大師第三名的排名。Ashish在跨領域項目中擁有豐富的經驗，涉及各種數據、平台和技術。
