Aileen Nielsen is a New York City-based software engineer and data analyst. She has worked on time series in a variety of disciplines, from a healthcare startup to a political campaign, from a physics research lab to a financial trading firm. She currently develops neural networks for forecasting applications.
Practical Time Series Analysis. Prediction with Statistics and Machine Learning
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- Retrait Click and Collect en magasin gratuit
- Nombre de pages480
- FormatGrand Format
- PrésentationBroché
- Poids0.808 kg
- Dimensions17,7 cm × 23,3 cm × 2,8 cm
- ISBN978-1-4920-4165-8
- EAN9781492041658
- Date de parution30/11/2019
- ÉditeurO'Reilly
Résumé
Time series data analysis is increasingly important due to the massive production of such data through the Internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques.
Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You'll get the guidance you need to confidently : Find and wrangle time series data ; Undertake exploratory time series data analysis ; Store temporal data ; Simulate time series data ; Generate and select features for a time series ; Measure error ; Forecast and classify time series with machine or deep learning ; Evaluate accuracy and performance.
Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You'll get the guidance you need to confidently : Find and wrangle time series data ; Undertake exploratory time series data analysis ; Store temporal data ; Simulate time series data ; Generate and select features for a time series ; Measure error ; Forecast and classify time series with machine or deep learning ; Evaluate accuracy and performance.