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.
Dr. Avishek Pal, PhD, is a software engineer, data scientist, author, and an avid Kaggler living in Hyderabad, the City of Nawabs, India. He has a bachelor of technology degree in industrial engineering from the Indian Institute of Technology (IIT) Kharagpur and has earned his doctorate in 2015 from University of Warwick, Coventry, United Kingdom. At Warwick, he studied at the prestigious Warwick Manufacturing Centre, which functions as one of the centers of excellence in manufacturing and industrial engineering research and teaching in UK.
In terms of work experience, Avishek has a diversified background. He started his career as a software engineer at IBM India to develop middleware solutions for telecom clients. This was followed by stints at a start-up product development company followed by Ericsson, a global telecom giant. During these three years, Avishek lived his passion for developing software solutions for industrial problems using Java and different database technologies.
Avishek always had an inclination for research and decided to pursue his doctorate after spending three years in software development. Back in 2011, the time was perfect as the analytics industry was getting bigger and data science was emerging as a profession. Warwick gave Avishek ample time to build up the knowledge and hands-on practice on statistical modeling and machine learning. He applied these not only in doctoral research, but also found a passion for solving data science problems on Kaggle.
After doctoral studies, Avishek started his career in India as a lead machine learning engineer for a leading US-based investment company. He is currently working at Microsoft as a senior data scientist and enjoys applying machine learning to generate revenue and save costs for the software giant.
Avishek has published several research papers in reputed international conferences and journals. Reflecting back on his career, he feels that starting as a software developer and then transforming into a data scientist gives him the end-to-end focus of developing statistics into consumable software solutions for industrial stakeholders.
Dr. PKS Prakash is a data scientist and author. He has spent the last 12 years in developing many data science solutions in several practice areas within the domains of healthcare, manufacturing, pharmaceutical, and e-commerce. He is working as the data science manager at ZS Associates. ZS is one of the world's largest business services firms, helping clients with commercial success by creating data-driven strategies using advanced analytics that they can implement within their sales and marketing operations in order to make them more competitive, and by helping them deliver an impact where it matters.
Prakash's background involves a PhD in industrial and system engineering from Wisconsin-Madison, US. He has earned his second PhD in engineering from University of Warwick, UK. His other educational qualifications involve a masters from University of Wisconsin-Madison, US, and bachelors from National Institute of Foundry and Forge Technology (NIFFT), India. He is the co-founder of Warwick Analytics spin-off from University of Warwick, UK.
Prakash has published articles widely in research areas of operational research and management, soft computing tools, and advance algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR among others. He has edited an issue on Intelligent Approaches to Complex Systems and contributed in books such as Evolutionary Computing in Advanced Manufacturing published by WILEY and Algorithms and Data Structures using R and R Deep Learning Cookbook published by PACKT.
