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Hands-on Time Series Analysis with Python in pdf


Download this PDF book: Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques 1st ed. Edition by B V Vishwas

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


Chapter 1: Time-Series Characteristics

Chapter 2: Data Wrangling and Preparation for Time Series

Chapter 3: Smoothing Methods 

Chapter 4: Regression Extension Techniques for Time-Series Data

Chapter 5: Bleeding-Edge Techniques

Chapter 6: Bleeding-Edge Techniques for Univariate Time 

Chapter 7: Bleeding-Edge Techniques for Multivariate Time Series

Chapter 8: Prophet

About the Author

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

About the book:

Publisher ‏ : ‎ Apress; 1st ed. edition (August 25, 2020)

Language ‏ : ‎ English

Pages ‏ : ‎ 428 

File : PDF, 12MB


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