Hands-on Time Series Analysis with Python

Hands-on Time Series Analysis with Python
Author :
Publisher : Apress
Total Pages : 407
Release :
ISBN-10 : 1484259912
ISBN-13 : 9781484259917
Rating : 4/5 (917 Downloads)

Book Synopsis Hands-on Time Series Analysis with Python by : B V Vishwas

Download or read book Hands-on Time Series Analysis with Python written by B V Vishwas and published by Apress. This book was released on 2020-08-25 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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


Hands-on Time Series Analysis with Python Related Books

Hands-on Time Series Analysis with Python
Language: en
Pages: 407
Authors: B V Vishwas
Categories: Computers
Type: BOOK - Published: 2020-08-25 - Publisher: Apress

GET EBOOK

Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approach
Hands-On Time Series Analysis with R
Language: en
Pages: 438
Authors: Rami Krispin
Categories: Computers
Type: BOOK - Published: 2019-05-31 - Publisher: Packt Publishing Ltd

GET EBOOK

Build efficient forecasting models using traditional time series models and machine learning algorithms. Key FeaturesPerform time series analysis and forecastin
Practical Time Series Analysis
Language: en
Pages: 500
Authors: Aileen Nielsen
Categories: Computers
Type: BOOK - Published: 2019-09-20 - Publisher: O'Reilly Media

GET EBOOK

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare,
Hands-On Exploratory Data Analysis with Python
Language: en
Pages: 342
Authors: Suresh Kumar Mukhiya
Categories: Computers
Type: BOOK - Published: 2020-03-27 - Publisher: Packt Publishing Ltd

GET EBOOK

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of expl
Machine Learning for Time-Series with Python
Language: en
Pages: 371
Authors: Ben Auffarth
Categories: Computers
Type: BOOK - Published: 2021-10-29 - Publisher: Packt Publishing Ltd

GET EBOOK

Get better insights from time-series data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods in