📈 A small, fast chart for time series, lines, areas, ohlc & bars
- time-series
- ohlc
- line-chart
- performance
Repository Index
47,133 repositories indexed
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A unified framework for machine learning with time series
- time-series
- machine-learning
- scikit-learn
- time-series-classification
A python library for user-friendly forecasting and anomaly detection on time series.
- python
- time-series
- forecasting
- machine-learning
The AI-native JSON Gantt chart engine. Dependency-free interactive timelines with React, script-tag, CLI, topological DAG solver, and formal JSON schema.
- ai-tools
- critical-path
- dag
- data-visualization
Algorithms for outlier, adversarial and drift detection
- anomaly
- outlier
- concept-drift
- detection
A plugin for the jQuery javascript library to generate small sparkline charts directly in the browser
Anomaly Detection and Correlation library
- anomaly-detection
- anomalydetection
Rumale is a machine learning library in Ruby
Glue strings to data in R. Small, fast, dependency free interpreted string literals.
- r
- strings
- string-interpolation
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
- data-analytics
- data-cleaning
- data-cleansing
- data-engineering
TTK - Topological Data Analysis and Visualization - Source Code
- topological-data-analysis
- visualization
- python
- paraview
Tidy anomaly detection
- detect-anomalies
- decomposition
- iqr
- time-series
AuShadha (औषध): Means medicine in Sanskrit. This is a Electronic Medical Records (EMR) and Public Health Management for small clinics written in Django and Dojo
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning
open source E-book on statistical physics
- statistical-physics
- statistical-mechanics
A minimal analytics tool to self-host.
Dataset of packed PE samples
- dataset
- pe-files
- upx
- samples
Graph plotting package with a small API and sensible defaults powered by gnuplot.
- gnuplot
- chart
- crystal
lit-line is a minimal line chart web component for modern websites.
Dataset of packed ELF samples
- samples
- dataset
- upx
- malware-analysis
Accelerated similarity searching of small molecules (bioconductor package)
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.