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UQMODELS
`UQMODELS` for time series is a python library that seeks to put into practice Uncertainty Quantification (UQ) based on ML/Deep learning models for the analysis of numerical data (Regression and time series). UQModels is inspired by scikit-learn for the creation of ML datascience processing chains incorporating uncertainty quantification mechanisms.
The main objective of these chains is to provide functionality that enhance confidence in model prediction by computing additional UQ-KPI that give insight about ML-modeling uncertainty throught for example : Predictive intervales/Margin of error that catch irreducible variability around an observation, or even local Model unreliability link to the impact of the lack of observation due to data-representativeness issues.
Therefore, the main functionality of the library concerns the modeling step of data science pipeline, through the implementation of an UQModel wrapper that can combine several implementations of forecasting models, UQestimators, and post-processor to form a mathematical processing chain. Then UQModel library provide :
Several UQEstimators able to estimate several nature of UQ-measure as statistical measure of ML-uncertainty and optionally make prediction.
UQ-Processing functions that process UQ-measure and/or prediction and/or observation to produce some usefull UQ-KPI.
Anom-processing functions that process UQ-measures and predictions and observations into a contextual deviation score based on residual (difference between observation and model) prediction normalised by UQmeasure thank to UQ-processing function.
UQKPI-Processor that wrap UQ-Processing function in a scikit learn Transformer format (fit/tranform procedure) to provide both UQ or Anom KPI
UQModel wrapper that combine a whole pipeline as a (fit/predict/score) object that produce both prediction and specified UQ-KPI
Such UQ-Model object can be used for example :
In the context of monitoring key variables, to characterize the state of a dynamic system
For time series monitoring, by providing forecast augmented by uncertainty KPIs (as error margin or model unreliability score) using predict method or anomaly score using score method.
In addition to modeling-post-processing pipeline, the library also implements minor functionality (in the form of wrappers) designed to facilitate the formalization of the pre-processing and evaluation step. The library aim is illustrated in the following figure.
UQMODELS is an initiative under Confiance.ai, an organization dedicated to fostering transparency, fairness, and trust in the field of artificial intelligence.