Practical Time Series Analysis (Paperback)

Step by Step guide filled with real world practical examples.

About This Book

• Get your first experience with data analysis with one of the most powerful types of analysis—time-series.
• Find patterns in your data and predict the future pattern based on historical data.
• Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide

Who This Book Is For

This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods.

What You Will Learn

• Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project
• Develop an understanding of loading, exploring, and visualizing time-series data
• Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series
• Take advantage of exponential smoothing to tackle noise in time series data
• Learn how to use auto-regressive models to make predictions using time-series data
• Build predictive models on time series using techniques based on auto-regressive moving averages
• Discover recent advancements in deep learning to build accurate forecasting models for time series
• Gain familiarity with the basics of Python as a powerful yet simple to write programming language

In Detail

Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.

The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.

The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.

Style and approach

This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

這本書是一本以實際案例為基礎的逐步指南，充滿了真實世界的實例。本書介紹了時間序列分析的基本概念、統計理論和實施方法，並提供了豐富的範例。

本書適合任何想要分析時間和/或頻率數據的人。需要具備統計背景以快速學習分析方法。

你將學到以下內容：
- 瞭解時間序列分析的基本概念，並認識它對數據科學項目成功的重要性
- 學習加載、探索和可視化時間序列數據的基本方法
- 探索自相關並瞭解處理非穩態時間序列的統計技術
- 利用指數平滑處理時間序列數據中的噪音
- 學習使用自回歸模型使用時間序列數據進行預測
- 使用基於自回歸移動平均的技術構建時間序列的預測模型
- 探索深度學習的最新進展，為時間序列構建準確的預測模型
- 熟悉Python作為一種強大且易於編寫的編程語言的基礎知識

時間序列分析允許我們分析在一段時間內生成的數據，並且觀察到觀測之間存在順序相依性。本書描述了特殊的數學技巧和技術，旨在探索時間序列數據的內部結構並生成有力的描述性和預測性見解。此外，本書還充滿了使用Python開發的尖端解決方案對時間序列及其分析的真實案例。

本書從描述性分析開始，創建具有趨勢、季節性和自相關等內部結構的有洞察力的可視化圖表。接下來，介紹處理自相關和非穩態時間序列的統計方法。然後，使用指數平滑從嘈雜的時間序列數據中獲得有意義的見解。在此基礎上，我們轉向預測分析，介紹自回歸移動平均（ARMA）和自回歸整合移動平均（ARIMA）等時間序列預測的自回歸模型。隨後，介紹強大的深度學習方法，以開發複雜時間序列的準確預測模型，即使在缺乏領域知識的情況下也能實現。所有主題都以真實問題場景和Python的最佳實踐實現解決方案進行說明。

本書以附錄結束，簡要討論使用Python編程和解決數據科學問題的方法。

本書以非常實用和真實世界的用例，將讀者從基礎水平引導到高級水平的時間序列分析。