Packt - Time Series Analysis with Python 3.x

[ FreeCourseWeb.com ] Time Series Analysis with Python 3.x



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Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48kHz, 2ch | Duration: 3h 23m

Genre: eLearning | Language: English | Size: 732 MB



A hands-on definitive guide to working with time series data

Learn

Key pandas concepts and techniques for time-based analysis

Study and work with important components of time series data such as trends, seasonality, and noise

Apply commonly used machine learning models for analysis

How to de-trend and de-seasonlize time series data

Manipulate data with AR, MA, and ARMA

Decompose time series data into its components for efficient analysis

Create an end-to-end anomaly detection project based on time series

About

Time series analysis encompasses methods for examining time series data found in a wide variety of domains. Being equipped to work with time-series data is a crucial skill for data scientists. In this course, you'll learn to extract and visualize meaningful statistics from time series data. You'll apply several analysis methods to your project. Along the way, you'll learn to explore, analyze, and predict time series data.



You'll start by working with pandas' datetime and finding useful ways to extract data. Then you'll be introduced to correlation/autocorrelation time-series relationships and detecting anomalies. You'll learn about autoregressive (AR) models and Moving Average (MA) models for time series, and explore anomalies in detail. You'll also discover how to blend AR and MA models to build a robust ARMA model. You'll also grasp how to build time series forecasting models using ARIMA. Finally, you'll complete your own project on time series anomaly detection.



By the end of this practical tutorial, you'll have acquired the skills you need to perform time series analysis using Python.



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