Practical Time Series Analysis

Practical Time Series Analysis: Prediction with Statistics and Machine Learning 1st Edition
Description:
Practical Time Series Analysis: Prediction with Statistics and Machine Learning by Aileen Nielsen is an essential guide for anyone interested in analyzing time series data. As the generation of time-dependent data grows rapidly—due to the Internet of Things (IoT), healthcare digitalization, and smart city developments—the ability to analyze this data becomes critical. This book provides readers with the tools needed to confidently approach time series data, utilizing both traditional statistical methods and modern machine learning techniques.

Nielsen offers a step-by-step introduction to time series analysis using R and Python, making the material accessible to data scientists, engineers, and researchers. Throughout the book, real-world use cases demonstrate how to solve common time series challenges, allowing readers to apply these techniques in practical settings.

Key Features:

• Learn to find, clean, and manage time series data.
• Perform exploratory data analysis on temporal datasets.
• Generate and select features specific to time series data.
• Simulate and forecast time series using machine learning and deep learning models.
• Evaluate the performance and accuracy of time series models.

What is Time Series Analysis with Machine Learning?
Time series analysis with machine learning refers to the use of advanced algorithms to model and predict sequential data points that are recorded over time. Unlike traditional methods, machine learning techniques can handle large datasets and complex patterns, making them highly effective in modern applications like financial forecasting, healthcare monitoring, and environmental data tracking.

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Product Details:
Publisher: O’Reilly Media
Published: September 20, 2019
ISBNs: 9781492041603, 9781492041627
Language: English
Print Length: 506 pages
Format: PDF