AutoStat®’s engine room. Undertake a range of advanced analytics with your data, and apply machine learning, statistics, time-series and survival modelling without needing to code. Bayesian optimisation with AutoML for automated variable selection and parameter tuning to enhance model performance.
01 - Statistical Frameworks
Model a wide range of data using linear regression & mixed models, along with a range of generalised linear models, including Logistic, Probit, Poisson, Logit and Tobit, plus extensions for mixed effects models.
02 - Machine Learning and Clustering
Access a range of supervised and unsupervised machine learning algorithms for both regression and classification problems. Apply clustering with Gaussian mixture models and a number of ML clustering algorithms.
03 - Multivariate Modelling
Make prediction models across many combinations of variables. Including dynamic factor models, factor analysis, multiple regression and linear discriminant analysis.
04 - Time-Series Modelling
Extract forecasts and identify trends with some of the most advanced time-series forecasting models including ARIMA, S-ARIMA, and Bayesian Unobserved Component models.
05 - Survival Analysis
Analyse failure points and durations of variables using a range of parametric and non-parametric algorithms.
06 - Results Analytics
Analyse model performance; identify key variables and their relative importances; and extract predictions for real-world insights.
From data processing and visualisation, to machine learning and beyond, get up to speed with the most advanced data science techniques with real-world projects using AutoStat®.