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Codeless Time Series Analysis with KNIME

Codeless Time Series Analysis with KNIME

By : KNIME AG , Corey Weisinger, Maarit Widmann, Daniele Tonini
4.8 (10)
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Codeless Time Series Analysis with KNIME

Codeless Time Series Analysis with KNIME

4.8 (10)
By: KNIME AG , Corey Weisinger, Maarit Widmann, Daniele Tonini

Overview of this book

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.
Table of Contents (20 chapters)
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1
Part 1: Time Series Basics and KNIME Analytics Platform
7
Part 2: Building and Deploying a Forecasting Model
14
Part 3: Forecasting on Mixed Platforms

Summary

In this chapter, we introduced popular techniques for the visual exploration of time series data. We started from a line plot, which shows simple dynamics of time series, and moved on to a lag plot to explore the relationship between past and current values. After that, we compared seasonal cycles in parallel in a seasonal plot, and finally, we inspected the variability of a time series in a box plot.

You learned how to interpret the dynamics in these plots and how they enrich your understanding of time series. You also learned how to implement these visualization techniques in KNIME Analytics Platform.

These visualizations appear in the data exploration phase of most time series analysis applications. You will need them to inspect whether the time series is periodic or shows a trend, to evaluate the model fit of an AR model, to obtain the best seasonal lag for prediction and missing value replacement, to assess the type of seasonality, and to visually evaluate the stationarity...

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