Practical Time Series Analysis [Video]

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Practical Time Series Analysis [Video]

Dr. Avishek Pal, Dr. PKS Prakash

4 customer reviews
Step-by-step guide filled with real-world practical examples
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Video Details

ISBN 139781788995719
Course Length2 hours and 25 minutes

Video Description

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
The Course Overview
Different Types of Data
Internal Structures of Time Series
Models for Time Series Analysis
Autocorrelation and Partial Autocorrelation
Understanding Time Series Data
Advanced Processing and Visualization of Time Series Data
Resampling Time Series Data
Stationary Processes
Time Series Decomposition
Exponential Smoothing Based Methods
Introduction to Time Series Smoothing
First Order Exponential Smoothing
Second Order Exponential Smoothing
Modeling Higher-Order Exponential Smoothing
Auto-Regressive Models
Working with AR Models
Moving Average Models
Building Datasets with ARMA
Building Datasets with ARIMA
Deep Learning for Time Series Forecasting
Air Pressure Time Series Forecasting
PM 2.5 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

Table of Contents

Introduction to Time Series
The Course Overview
Different Types of Data
Internal Structures of Time Series
Models for Time Series Analysis
Autocorrelation and Partial Autocorrelation
Understanding Time Series Data
Advanced Processing and Visualization of Time Series Data
Resampling Time Series Data
Stationary Processes
Time Series Decomposition
Exponential Smoothing Based Methods
Introduction to Time Series Smoothing
First Order Exponential Smoothing
Second Order Exponential Smoothing
Modeling Higher-Order Exponential Smoothing
Auto-Regressive Models
Working with AR Models
Moving Average Models
Building Datasets with ARMA
Building Datasets with ARIMA
Deep Learning for Time Series Forecasting
Air Pressure Time Series Forecasting
PM 2.5 Time Series Forecasting

Video Details

ISBN 139781788995719
Course Length2 hours and 25 minutes
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