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B V Vishwas - Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques

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B V Vishwas Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
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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.

Youll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, youll 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 , youll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more.
What Youll Learn * * Explains basics to advanced concepts of time series. * How to design, develop, train, test 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 prepration 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

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B V Vishwas - Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
B V Vishwas
Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques

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B V Vishwas and Ashish Patel Hands-on Time Series Analysis with Python From - photo 1
B V Vishwas and Ashish Patel
Hands-on Time Series Analysis with Python
From Basics to Bleeding Edge Techniques
1st ed.
B V Vishwas Infosys Bengaluru India Ashish Patel Cygnet Infotech Pvt Ltd - photo 2
B V Vishwas
Infosys, Bengaluru, India
Ashish Patel
Cygnet Infotech Pvt Ltd, Ahmedabad, India

Any source code or other supplementary material referenced by the author in this Book is available to readers on GitHub via the Books product page, located at www.apress.com/978-1-4842-5991-7 . For more detailed information, please visit www.apress.com/source-code .

ISBN 978-1-4842-5991-7 e-ISBN 978-1-4842-5992-4
https://doi.org/10.1007/978-1-4842-5992-4
B V Vishwas and Ashish Patel 2020
Apress Standard
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.
Introduction

This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples.

The book begins by covering time-series fundamentals and their characteristics, Structure & Components of time series data, preprocessing, and ways of crafting features through data wrangling. Next, it covers traditional time-series techniques such as the smoothing methods ARMA, ARIMA, SARIMA, SARIMAX, VAR, and VARMA using trending frameworks such as Statsmodels and Pmdarima.

Further covers how to leverage advanced deep learning-based techniques such as ANN, CNN, RNN, LSTM, GRU, and Autoencoder to solve time-series problems using Tensorflow. It concludes by explaining how to use the popular framework fbprophet for modeling time-series analysis.

After completion of the book, the reader will have thorough knowledge of concepts and techniques to solve time-series problems. All the code presented in this book is available in Jupyter Notebooks; this allows readers to do hands-on experiments and enhance them in exciting ways.

Acknowledgments

This being my first book, I found transforming idea and real-world experience into its current shape to be a strenuous task. I am grateful to almighty God for blessing and guiding me in all endeavors. I would like to thank my parents (Vijay Kumar and Rathnamma), brother (Shreyas), other family, and friends for helping me sail though the sea of life.

B V Vishwas

First and foremost, praises and thanks to God, the almighty, for His showers of blessings throughout the book-writing process. I would like to express my deep and sincere gratitude to my parents (Dinesh Kumar N Patel and Javnika ben D Patel), my sister (Nisha Patel), and my family and friends (Shailesh Patel, Sanket Patel, Nikit Patel, Mansi Patel, Khushboo Shah) for their support and valuable prayers.

Ashish Patel

Special thanks to Celestin Suresh John, Aditee Mirashi, James Markham, Alexey Panchekha, and the Apress team for bringing this book to life.

Table of Contents
About the Authors
B V Vishwas
is a Data Scientist AI researcher and AI Consultant Currently living in - photo 3

is a Data Scientist, AI researcher and AI Consultant, Currently living in Bengaluru(INDIA). His highest qualification is Master of Technology in Software Engineering from Birla Institute of Technology & Science, Pilani, India 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 & 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 - photo 4

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.

About the Technical Reviewer
Alexey Panchekha
Over his nearly three-decade career Alexey Panchekha PhD CFA has spent 10 - photo 5

Over his nearly three-decade career, Alexey Panchekha, PhD, CFA, has spent 10 years in academia, where he focused on nonlinear and dynamic processes; 10 years in the technology industry, where he specialized in program design and development; and eight years in financial services. In the latter arena, he specialized in applying mathematical techniques and technology to risk management and alpha generation. For example, Panchekha was involved in the equity derivative trading technology platform at Goldman Sachs, and he led the creation of the multi-asset multigeographies portfolio risk management system at Bloomberg. He also served as the head of research at Markov Process International, a leader in portfolio attribution and analytics. Most recently, Panchekha cofounded Turing Technology Associates, Inc., with Vadim Fishman. Turing is a technology and intellectual property company that sits at the intersection of mathematics, machine learning, and innovation. Its solutions typically service the financial technology (fintech) industry. Turing primarily focuses on enabling technology that supports the burgeoning ensemble active management (EAM). Prior to Turing, Panchekha was managing director at Incapital, and head of research at F-Squared Investments, where he designed innovative volatility-based risk-sensitive investment strategies. He is fluent in multiple computer programming languages and software and database programs and is certified in deep learning software. He earned a PhD from Kharkiv Polytechnic University with studies in physics and mathematics as well as an MS in physics. Panchekha is a CFA charterholder.

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