Real-Time Visualization in Machine Learning - Evaluating LightningChart Python and Competitor Libraries - Bachelor’s Thesis

Q: Which visualization libraries are benchmarked, and for what task?

The thesis benchmarks LightningChart Python (LC PY), Bokeh, and Plotly for real-time machine learning visualization using an LSTM-based stock price prediction stream.

Q: What metrics are used to evaluate real-time visualization performance?

Performance is evaluated using frames per second (FPS), FPS stability (standard deviation), CPU usage, memory usage, and render time.

Q: How do the libraries compare across different data scales?

LC PY performs best across all tested scales, Bokeh is best for small to medium datasets (up to about 10,000 points) but declines after 100,000, and Plotly is adequate for smaller datasets but unsuitable for real-time visualization after 10,000 points.

Aug 4, 2026

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The thesis evaluates the performance of LightningChart Python (LC PY), Bokeh, and Plotly for real-time machine learning visualization across data scales from 1,000 to 1,000,000 points. Libraries are benchmarked using frames per second (FPS), FPS stability, CPU and memory usage, and render time to emulate real-time streams produced by an LSTM model predicting stock prices. The study finds LC PY best overall scalability, with ~55 average FPS and very stable results (std FPS <1) across large scales. Bokeh performs strongly for up to 10,000 points but degrades beyond 100,000, while Plotly becomes unsuitable above 10,000 points.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages53

Reading Time2 h 14 min

AccessFree to download

Introduction

Overview and Importance of Data Visualization

Types of Data Visualization Libraries

Libraries Used in the Study

Overview of Machine Learning

Performance in Data Visualization

Methodology and Research Design

Tools and Technologies

Data Collection and Machine Learning Model Development

Performance Metrics and Benchmarking Procedure

Data Analysis

Implementation and Analytical Comparison of Models

Stock Price Prediction with LSTM Model

Data Visualization Benchmarking Framework

Tests’ Consistency between Libraries

Real-Time Visualization in Machine Learning Evaluating LightningChart Python and Competitor Libraries Bachelor’s Thesis Degree Programme in Computer Applications Spring 2025 Ahmad Omid DP Degree Programme in Computer Applications Abstract Author Ahmad Omid Year 2025 Subject Real-Time Visualization in Machine Learning: Evaluating LightningChart Python and Competitor Libraries Supervisors Mazhar Mohsin The thesis evaluates the performance of LightningChart Python (LC PY), Bokeh, and Plotly for realtime machine learning (ML) visualization across data scales from 1,000 to 1,000,000 points, which meets a need for scalable and effective visualization tools in machine learning applications. The study benchmarks these libraries using metrics such as frames per second (FPS), stability (standard deviation of FPS), CPU and memory usage, and render time, to simulate real-time data streams from an LSTM model predicting stock prices into the charts to get the metrics’ results. Results show that LC PY was the best among all scales, supporting an average FPS of ~55 with excellent stability (std FPS <1), despite higher resource usage (CPU 35–56%, memory up to 2.63% at 1,000,000 points), because of its outstanding scalability. Bokeh delivers very good performance for small to medium datasets (up to 10,000 points, FPS ~105, CPU <3 .5%), but its suitability declines above 100,000 points (FPS drops to 5) . Plotly performs adequately for smaller datasets (FPS 46–49, CPU <5 . 13%) but becomes unsuitable for real-time visualization after 10,000 points (FPS 1.23 at 1,000,000) . Based on these findings, the thesis recommends LC PY for large-scale and very largescale ML tasks (100,000+ points), Bokeh or Plotly for small to medium scales (up to 10,000 points), and LC PY or Bokeh for intermediate scales (10,000–100,000 points) . Keywords Machine Learning, LSTM, LightningChart Python, Bokeh, Plotly, Data Visualizations, RealTime Data Pages 45 pages and appendices 1 page Glossary ML LC PY RNN FNN LSTM FPS JSON WebGL API HTML GPU CPU CSV Machine Learning LightningChart Python Recurrent Neural Networks Feed-Forward Neural Networks Long Short-Term Memory Frames Per Second JavaScript Object Notation Web Graphics Library Application Programming Interface Hypertext Markup Language Graphics Processing Unit Central Processing Unit Comma-Separated Values Table of Contents 1 Introduction ..................................................................................................................................... 1 2 Real-Time Visualization in Machine Learning .................................................................................. 2 2.1 Overview and Importance of Data Visualization ..................................................................... 2 2.1.1 Types of Data Visualization Libraries .......................................................................... 3 2.1.2 Libraries Used in the Study ......................................................................................... 4 2.1.2.1 LightningChart Python ................................................................................................ 4 2.1.2.2 Bokeh ......................................................................................................................... 5 2.1.2.3 Plotly .......................................................................................................................... 5 2.2 Overview of Machine Learning ............................................................................................... 6 2.2.1 Deep Learning ............................................................................................................ 8 2.2.2 Long Short-Term Memory (LSTM) .............................................................................. 9 2.2.3 Importance of Real-Time Data in ML ...........................................................

FAQs

Which visualization libraries are benchmarked, and for what task?

The thesis benchmarks LightningChart Python (LC PY), Bokeh, and Plotly for real-time machine learning visualization using an LSTM-based stock price prediction stream.

What metrics are used to evaluate real-time visualization performance?

Performance is evaluated using frames per second (FPS), FPS stability (standard deviation), CPU usage, memory usage, and render time.

How do the libraries compare across different data scales?

LC PY performs best across all tested scales, Bokeh is best for small to medium datasets (up to about 10,000 points) but declines after 100,000, and Plotly is adequate for smaller datasets but unsuitable for real-time visualization after 10,000 points.

Aug 4, 2026

Upload by Sage