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  • © 2020

Hands-on Time Series Analysis with Python

From Basics to Bleeding Edge Techniques

Apress
  • Covers latest time series packages like fbprophet and pmdarima.
  • Introduces reader’s to wide range of methods such as Smoothening, ARIMA, SARIMA, SARIMAX, VAR, VARMA, AUTO-ARIMA
  • Explains how to leverage advance deep learning based techniques like RNN, LSTM, CNN

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xvii
  2. Time-Series Characteristics

    • B V Vishwas, Ashish Patel
    Pages 1-21
  3. Data Wrangling and Preparation for Time Series

    • B V Vishwas, Ashish Patel
    Pages 23-64
  4. Smoothing Methods

    • B V Vishwas, Ashish Patel
    Pages 65-97
  5. Regression Extension Techniques for Time-Series Data

    • B V Vishwas, Ashish Patel
    Pages 99-184
  6. Bleeding-Edge Techniques

    • B V Vishwas, Ashish Patel
    Pages 185-226
  7. Bleeding-Edge Techniques for Univariate Time Series

    • B V Vishwas, Ashish Patel
    Pages 227-324
  8. Bleeding-Edge Techniques for Multivariate Time Series

    • B V Vishwas, Ashish Patel
    Pages 325-374
  9. Prophet

    • B V Vishwas, Ashish Patel
    Pages 375-394
  10. Back Matter

    Pages 395-407

About this book

Learn the concepts of time series from traditional to bleeding-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:

·  Explains basics to advanced concepts of time series

·  How to design, develop, train, and validate time-series methodologies

·  What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results

·  Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder  to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.

·  Univariate and multivariate problem solving using fbprophet.




Who This Book Is For


Data scientists, data analysts, financial analysts, and stock market researchers






Authors and Affiliations

  • Infosys, Bengaluru, India

    B V Vishwas

  • Cygnet Infotech Pvt Ltd, Ahmedabad, India

    ASHISH PATEL

About the authors

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 andhis 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

Bibliographic Information

  • Book Title: Hands-on Time Series Analysis with Python

  • Book Subtitle: From Basics to Bleeding Edge Techniques

  • Authors: B V Vishwas, ASHISH PATEL

  • DOI: https://doi.org/10.1007/978-1-4842-5992-4

  • Publisher: Apress Berkeley, CA

  • eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)

  • Copyright Information: B V Vishwas and Ashish Patel 2020

  • Softcover ISBN: 978-1-4842-5991-7Published: 25 August 2020

  • eBook ISBN: 978-1-4842-5992-4Published: 24 August 2020

  • Edition Number: 1

  • Number of Pages: XVII, 407

  • Number of Illustrations: 424 b/w illustrations

  • Topics: Machine Learning, Python, Open Source

Buy it now

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access