Step-by-step guide filled with real-world practical examples
About This Video
- Gain experience with powerful types of data analysis —time series.
- Find patterns in your data and predict future patterns based on historical data.
- Learn the statistics, and implementation of Time Series methods using this rich guide
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.
Table of Contents
- Chapter 1 : Introduction to Time Series
Chapter 2 : Understanding Time Series Data
- Advanced Processing and Visualization of Time Series Data 00:02:06
- Resampling Time Series Data 00:06:56
- Stationary Processes 00:12:36
- Time Series Decomposition 00:14:08
- Chapter 3 : Exponential Smoothing Based Methods
- Chapter 4 : Auto-Regressive Models
- Chapter 5 : Deep Learning for Time Series Forecasting
- Title: Practical Time Series Analysis
- Release date: December 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788995719