Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications (Paperback)

Perform time series analysis using KNIME Analytics Platform, covering both statistical methods and machine learning-based methods

Key Features

- Gain a solid understanding of time series analysis and its applications using KNIME
- Learn how to apply popular statistical and machine learning time series analysis techniques
- Integrate other tools such as Spark, H2O, and Keras with KNIME within the same application

Book Description

This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques.

This learning journey is organized in a crescendo of difficulty, starting from the easiest yet effective techniques applied to weather forecasting, then introducing ARIMA and its variations, moving on to machine learning for audio signal classification, training deep learning architectures to predict glucose levels and electrical energy demand, and ending with an approach to anomaly detection in IoT. There's no time series analysis book without a solution for stock price predictions and you'll find this use case at the end of the book, together with a few more demand prediction use cases that rely on the integration of KNIME Analytics Platform and other external tools.

By the end of this time series book, you'll have learned about popular time series analysis techniques and algorithms, KNIME Analytics Platform, its time series extension, and how to apply both to common use cases.

What you will learn

- Install and configure KNIME time series integration
- Implement common preprocessing techniques before analyzing data
- Visualize and display time series data in the form of plots and graphs
- Separate time series data into trends, seasonality, and residuals
- Train and deploy FFNN and LSTM to perform predictive analysis
- Use multivariate analysis by enabling GPU training for neural networks
- Train and deploy an ML-based forecasting model using Spark and H2O

Who this book is for

This book is for data analysts and data scientists who want to develop forecasting applications on time series data. While no coding skills are required thanks to the codeless implementation of the examples, basic knowledge of KNIME Analytics Platform is assumed. The first part of the book targets beginners in time series analysis, and the subsequent parts of the book challenge both beginners as well as advanced users by introducing real-world time series applications.

使用KNIME Analytics Platform進行時間序列分析，包括統計方法和基於機器學習的方法。

主要特點：
- 通過KNIME獲得對時間序列分析及其應用的扎實理解
- 學習如何應用流行的統計和機器學習時間序列分析技術
- 在同一應用程序中將其他工具（如Spark、H2O和Keras）與KNIME集成

書籍描述：
本書將帶您進行實用的旅程，教您如何實施涉及時間序列分析技術的多種用例解決方案。

這個學習旅程按照難度逐漸增加的方式組織，從應用於天氣預報的最簡單但有效的技術開始，然後介紹ARIMA及其變體，接著是用於音頻信號分類的機器學習，訓練深度學習架構以預測葡萄糖水平和電能需求，最後介紹物聯網中的異常檢測方法。沒有時間序列分析書籍可以沒有股價預測解決方案，您將在本書的最後找到這個用例，以及其他一些依賴於KNIME Analytics Platform和其他外部工具的需求預測用例。

通過閱讀本書，您將學習到流行的時間序列分析技術和算法，KNIME Analytics Platform及其時間序列擴展，以及如何將它們應用於常見的用例。

您將學到：
- 安裝和配置KNIME時間序列集成
- 在分析數據之前實施常見的預處理技術
- 以圖表和圖形的形式可視化和顯示時間序列數據
- 將時間序列數據分為趨勢、季節性和殘差
- 訓練和部署FFNN和LSTM進行預測分析
- 通過啟用GPU訓練神經網絡來使用多變量分析
- 使用Spark和H2O訓練和部署基於機器學習的預測模型

本書適合數據分析師和數據科學家，他們希望在時間序列數據上開發預測應用程序。由於示例的無代碼實現，不需要編程技能，但需要基本的KNIME Analytics Platform知識。本書的第一部分針對時間序列分析的初學者，後續部分則通過介紹現實世界的時間序列應用挑戰初學者和高級用戶。

1. Introducing Time Series Analysis
2. Introduction to KNIME Analytics Platform
3. Preparing Data for Time Series Analysis
4. Time Series Visualization
5. Time Series Components and Statistical Properties
6. Humidity Forecasting with Classical Methods
7. Forecasting the Temperature with ARIMA and SARIMA Models
8. Audio Signal Classification with an FFT and a Gradient Boosted Forest
9. Training and Deploying a Neural Network to Predict Glucose Levels
10. Predicting Energy Demand with an LSTM Model
11. Anomaly Detection – Predicting Failure with No Failure Examples
12. Predicting Taxi Demand on the Spark Platform
13. GPU Accelerated Model for Multivariate Forecasting
14. Combining KNIME and H2O to Predict Stock Prices

1. 引言時間序列分析
2. KNIME Analytics 平台介紹
3. 準備時間序列分析的資料
4. 時間序列視覺化
5. 時間序列組件和統計特性
6. 使用傳統方法進行濕度預測
7. 使用 ARIMA 和 SARIMA 模型進行溫度預測
8. 使用 FFT 和梯度提升森林進行音訊信號分類
9. 訓練並部署神經網路以預測葡萄糖水平
10. 使用 LSTM 模型預測能源需求
11. 異常檢測 - 在沒有故障示例的情況下預測故障
12. 在 Spark 平台上預測計程車需求
13. 多變量預測的 GPU 加速模型
14. 結合 KNIME 和 H2O 預測股票價格