Packt – Practical time series analysis video | 610.86 MB
Time Series Analysis allows us to analyze data that is generated over a period of time and has sequential interdependencies between the observations. This video describes special mathematical tricks and techniques that are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the tutorial is full of real-life time series examples and their analyses using cutting-edge solutions developed in Python. The video starts with a 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 the 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. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.
Style and Approach This course takes viewers from basic to advanced time series analysis in a very practical way, and with real-world use cases.
Table of Contents INTRODUCTION TO TIME SERIES UNDERSTANDING TIME SERIES DATA EXPONENTIAL SMOOTHING BASED METHODS AUTO-REGRESSIVE MODELS DEEP LEARNING FOR TIME SERIES FORECASTING What You Will Learn Understand the basic concepts of Time Series Analysis Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and master statistical techniques Take advantage of exponential smoothing to tackle noise in time series data Learn to use auto-regressive models to make predictions using time series data Build predictive models on time series data using techniques based on auto-regressive moving averages Authors Dr. Avishek Pal Dr. Avishek Pal, PhD, is a software engineer, data scientist, author, and an avid Kaggler living in Hyderabad, India. He achieved his Bachelor of Technology degree in industrial engineering from the Indian Institute of Technology (IIT) Kharagpur and earned his doctorate in 2015 from University of Warwick, Coventry, United Kingdom.
He started his career as a software engineer at IBM India developing middleware solutions for telecom clients. This was followed by stints at a start-up product development company followed by Ericsson, the global telecom giant.
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.
Avishek has published several research papers in reputed international conferences and journals.
Dr. PKS Prakash Dr. PKS Prakash is a data scientist and author.
He has spent the last 12 years in developing many data science solutions in several practical areas in healthcare, manufacturing, pharmaceuticals, and e-commerce. He currently works as the data science manager at ZS Associates. He is the co-founder of Warwick Analytics, a spin-off from University of Warwick, UK. Prakash has published articles widely in research areas of operational research and management, soft computing tools, and advanced algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR, among others. He has edited an article on Intelligent Approaches to Complex Systems and contributed to 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.