Practical Time Series Analysis

Released on by Dr. Avishek Pal
preview-18

  • Practical Time Series Analysis Book Detail

  • Author : Dr. Avishek Pal
  • Release Date : 2017-09-28
  • Publisher : Packt Publishing Ltd
  • Genre : Computers
  • Pages : 238
  • ISBN 13 : 178829419X
  • File Size : 31,31 MB
DOWNLOAD BOOK

Practical Time Series Analysis by Dr. Avishek Pal PDF Summary

Book Description: Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

Disclaimer: www.yourbookbest.com does not own Practical Time Series Analysis books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Practical Time Series Analysis

Practical Time Series Analysis

File Size : 75,75 MB
Total View : 6826 Views
DOWNLOAD

Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types o

R Data Structures and Algorithms

R Data Structures and Algorithms

File Size : 97,97 MB
Total View : 5937 Views
DOWNLOAD

Increase speed and performance of your applications with efficient data structures and algorithms About This Book See how to use data structures such as arrays,

R Deep Learning Cookbook

R Deep Learning Cookbook

File Size : 49,49 MB
Total View : 2253 Views
DOWNLOAD

Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book Master intricacies of R deep learni

R Deep Learning Essentials

R Deep Learning Essentials

File Size : 40,40 MB
Total View : 1240 Views
DOWNLOAD

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and tex

Hands-On Ensemble Learning with R

Hands-On Ensemble Learning with R

File Size : 56,56 MB
Total View : 8196 Views
DOWNLOAD

Explore powerful R packages to create predictive models using ensemble methods Key Features Implement machine learning algorithms to build ensemble-efficient mo