Statistics and Machine Learning Applications
Statistics and Machine Learning Toolbox™ provides tools to describe, analyze, and model data. Apply these tools, in combination with other MATLAB® toolboxes, to perform industry-specific workflows. Some of the application areas include:
Aerospace — Explore radar and other signals, detect anomalies, and build predictive models.
Biotechnology and Pharmaceutical — Analyze clinical data, and perform modeling and simulation for drug discovery and development.
Communications and Signal Processing — Classify audio and other signals, and model wireless devices and integrated circuits.
Energy Production — Forecast energy demand, monitor production equipment, and optimize processing of chemicals in oil and gas.
Industrial Automation and Machinery — Apply multivariate statistics and predictive modeling to industrial process data, monitor manufacturing processes and product quality, and improve utilization and yields.
Medical Devices — Build interpretable machine learning algorithms on biomedical time series and image data for developing applications while complying with regulatory standards.
Quantitative Finance and Risk Management — Train, compare, and optimize models for algorithmic trading, asset allocation, credit risk, and fraud detection.
Classify radar returns using machine and deep learning approaches.
Biotechnology and Pharmaceutical
Gene Expression Profile Analysis (Bioinformatics Toolbox)
This example shows a number of ways to look for patterns in gene expression profiles.
Identifying Significant Features and Classifying Protein Profiles (Bioinformatics Toolbox)
This example shows how to classify mass spectrometry data and use some statistical tools to look for potential disease markers and proteomic pattern diagnostics.
Drug Discovery and Development
This example shows how to perform a Monte Carlo simulation of a pharmacokinetic/pharmacodynamic (PK/PD) model for an antibacterial agent.
Simulating the Glucose-Insulin Response (SimBiology)
This example shows how to simulate and analyze a model in SimBiology® using a physiologically based model of the glucose-insulin system in normal and diabetic humans.
Communications and Signal Processing
Data Analysis on S-Parameters of RF Data Files (RF Toolbox)
This example shows how to perform statistical analysis on a set of S-parameter data files using magnitude, mean, and standard deviation (STD).
Wavelet Time Scattering with GPU Acceleration — Spoken Digit Recognition (Wavelet Toolbox)
Extract features on your GPU for signal classification.
Predictive Analytics for Asset Management
Wind Turbine High-Speed Bearing Prognosis (Predictive Maintenance Toolbox)
Build an exponential degradation model to predict the Remaining Useful Life (RUL) of a wind turbine bearing in real time. The exponential degradation model predicts the RUL based on its parameter priors and the latest measurements.
Energy Trading and Risk Management (ETRM)
Model and Simulate Electricity Spot Prices Using the Skew-Normal Distribution (Econometrics Toolbox)
This example shows how to simulate the future behavior of electricity spot prices from a time series model fitted to historical data.
Hedging Strategies Using Spread Options (Financial Instruments Toolbox)
This example shows different hedging strategies to minimize exposure in the Energy market using Crack Spread Options.
Pricing Swing Options Using the Longstaff-Schwartz Method (Financial Instruments Toolbox)
This example shows how to price a swing option using a Monte Carlo simulation and the Longstaff-Schwartz method.
Industrial Automation and Machinery
Fault Detection Using Data Based Models (Predictive Maintenance Toolbox)
Use a data-based modeling approach for fault detection.
Rolling Element Bearing Fault Diagnosis (Predictive Maintenance Toolbox)
Perform fault diagnosis of a rolling element bearing based on acceleration signals. Apply envelope spectrum analysis and spectral kurtosis to fault diagnosis on bearings.
Fault Diagnosis of Centrifugal Pumps Using Residual Analysis (Predictive Maintenance Toolbox)
Use a model parity-equations-based approach for detection and diagnosis of faults in a pumping system.
Air Compressor Fault Detection Using Wavelet Scattering (Wavelet Toolbox)
Classify faults in acoustic recordings of air compressors using a wavelet scattering network and a support vector machine.
Wavelet Time Scattering for ECG Signal Classification (Wavelet Toolbox)
Classify human electrocardiogram signals using wavelet time scattering and a support vector machine classifier.
Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
Generate code from a classification Simulink® model prepared for deployment to a smartphone.
Generate code from a classification Simulink model prepared for fixed-point deployment.
Quantitative Finance and Risk Management
Machine Learning for Statistical Arbitrage: Introduction (Financial Toolbox)
This topic introduces a series of examples that provide a general workflow for illustrating how capabilities in MATLAB apply to statistical arbitrage.
Create a continuous-time Markov model of limit order book (LOB) dynamics, and develop a strategy for algorithmic trading based on patterns observed in the data.
Forecasting Corporate Default Rates (Financial Toolbox)
This example shows how to build a forecasting model for corporate default rates.
Comparison of Credit Scoring Using Logistic Regression and Decision Trees (Risk Management Toolbox)
This example shows the workflow for creating and comparing two credit scoring models: a credit scoring model based on logistic regression and a credit scoring model based on decision trees.
Portfolio Optimization and Asset Allocation
Portfolio Optimization Using Factor Models (Financial Toolbox)
This example shows two approaches for using a factor model to optimize asset allocation under a mean-variance framework.
Time Series Regression I: Linear Models (Econometrics Toolbox)
This example introduces basic assumptions behind multiple linear regression models.
Time Series Regression III: Influential Observations (Econometrics Toolbox)
This example shows how to detect influential observations in time series data and accommodate their effect on multiple linear regression models.