Time Series for Data Science: Analysis and Forecasting

Data Science students and practitioners want to find a forecast that "works" and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.

This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.

Features:

• Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.
• Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.
• Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.
• There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

《時間序列與資料科學：分析與預測》是一本討論集成模型技術的書籍，針對資料科學學生和從業人員尋找有效的預測方法，並不希望受限於單一的預測策略。本書涵蓋了時間序列回歸模型、指數平滑、Holt-Winters預測和神經網絡等技術。特別強調了經典的ARMA和ARIMA模型，這在其他相關教材中往往缺乏。

本書是一本易於理解的指南，不需要微積分背景也能引人入勝，但同時也不回避對所討論技術的深入解釋。

特點：
- 提供了對各種時間序列模型和方法的全面覆蓋和比較，包括指數平滑、Holt-Winters、ARMA和ARIMA，以及深度學習模型，如RNN、LSTM、GRU，以及由這些模型組合而成的集成模型。
- 引入了ARMA和ARIMA模型的因子表表示法。這種表示法在其他同級書籍中並不常見，對實踐和教學都非常有用。
- 使用了可以通過網頁鏈接輕鬆找到的真實世界示例，例如美國統計局、交通部和世界銀行等來源。
- 附帶有一個易於使用且不需要或僅需要很少的R經驗的R套件。該套件實現了本書中介紹的各種模型和方法，具有極大的教學用途。

Wayne Woodward, Bivin Sadler, Stephen Robertson

Wayne Woodward, Bivin Sadler, Stephen Robertson