Hands-on time series analysis with Python : from basics to bleeding edge techniques / by B.V. Vishwas, Ashish Patel

Format:

Book

Author/Creator:

Vishwas, B V., author.

Patel, Ash, author.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Open source software.

Machine Learning.

Python.

Open Source.

Local Subjects:

Machine Learning.

Python.

Open Source.

Physical Description:

1 online resource (420 pages)

Edition:

1st ed. 2020.

Place of Publication:

Berkeley, CA : Apress, [2020].

System Details:

Mode of access: World Wide Web.

text file

Summary:

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.

Contents:

Chapter 1: Time Series and its Characteristics

Chapter 2: Data Wrangling and Preparation for Time Series

Chapter 3: Smoothing Methods

Chapter 4: Regression Extension Techniques for Time Series

Chapter 5: Bleeding Edge Techniques

Chapter 6: Bleeding Edge Techniques for Univariate Time Series

Chapter 7: Bleeding Edge Techniques for Multivariate Time Series

Chapter 8: Prophet.

Notes:

Includes index.

Includes bibliographical references and index.

ISBN:

9781484259924

1484259920

OCLC:

1204218117