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

Become proficient in deriving insights from time-series data and analyzing a model's performance

Key Features:

• Explore popular and modern machine learning methods including the latest online and deep learning algorithms
• Learn to increase the accuracy of your predictions by matching the right model with the right problem
• Master time-series via real-world case studies on operations management, digital marketing, finance, and healthcare

Book Description:

Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.

This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.

Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.

By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.

What You Will Learn:

• Understand the main classes of time-series and learn how to detect outliers and patterns
• Choose the right method to solve time-series problems
• Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
• Get to grips with time-series data visualization
• Understand classical time-series models like ARMA and ARIMA
• Implement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning models
• Become familiar with many libraries like prophet, xgboost, and TensorFlow

Who this book is for:

This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.

成為從時間序列數據中獲取洞察並分析模型性能的熟練者

主要特點:
- 探索流行且現代的機器學習方法，包括最新的在線學習和深度學習算法
- 學習通過將正確的模型與正確的問題匹配來提高預測準確性
- 通過運營管理、數字營銷、金融和醫療保健等實際案例研究，掌握時間序列

書籍描述:
機器學習已成為理解時間序列數據中隱藏複雜性的強大工具，這些數據經常需要在醫療保健、經濟學、數字營銷和社會科學等各個領域進行分析。這些數據對於預測和預測結果，或者檢測異常以支持明智的決策至關重要。

本書涵蓋了時間序列的Python基礎知識，並建立了對傳統自回歸模型和現代非參數模型的理解。您將學會從任何來源加載時間序列數據集，使用深度學習模型，如循環神經網絡和因果卷積網絡模型，以及特徵工程的梯度提升。

《Python時間序列機器學習》解釋了幾種有用模型的理論，並指導您將正確的模型應用於正確的問題。該書還包括涵蓋天氣、交通、騎行和股票市場數據的實際案例研究。

通過閱讀本書，您將能夠熟練地應用機器學習原則分析時間序列數據集。

學到什麼:
- 理解時間序列的主要類別，並學習如何檢測異常和模式
- 選擇正確的方法來解決時間序列問題
- 通過自相關和統計技術來描述季節性和相關模式
- 掌握時間序列數據可視化
- 理解ARMA和ARIMA等傳統時間序列模型
- 實施高斯過程和變壓器等深度學習模型以及最先進的機器學習模型
- 熟悉prophet、xgboost和TensorFlow等多個庫

適合對象:
本書適合數據分析師、數據科學家和Python開發人員，他們希望進行時間序列分析以有效預測結果。基本的Python語言知識是必需的，熟悉統計學則更好。