Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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ASIN ‏ : ‎ B09GS44ZP4
Publisher ‏ : ‎ Packt Publishing; 1st edition (October 29, 2021)
Publication date ‏ : ‎ October 29, 2021
Language ‏ : ‎ English
File size ‏ : ‎ 16832 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 370 pages

Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

In today’s data-driven world, time-series data is everywhere, from stock prices and weather forecasts to sensor data and sales projections. Machine learning techniques have revolutionized the way we analyze and interpret time-series data, allowing us to forecast future trends, predict outcomes, and detect anomalies with unprecedented accuracy.

In this post, we will explore how to leverage Python and state-of-the-art machine learning methods to tackle time-series data analysis. We will cover techniques such as ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Prophet for forecasting, as well as isolation forests and one-class SVM for anomaly detection.

By the end of this post, you will have a solid understanding of how to apply machine learning to time-series data, enabling you to make better predictions, optimize resources, and detect anomalies in your data. Stay tuned for practical examples, code snippets, and hands-on exercises to help you master these powerful techniques. Let’s dive in and unlock the potential of machine learning for time-series data analysis!
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