Github repo for the research paper titled "Integrating Adaptive Moving Window and Just-in-Time Learning Paradigms for Soft-Sensor Design"

• Please cite the corresponding paper if you use the code, and do let me know if you encounter any errors/bugs!! I'd be more than glad to help debug. (note to self: add .bib entry when paper gets published)

• I tried my best to clean up the code. But keep in mind that the code has gone through countless changes during my MS thesis, so you might see a few unnecessary variables defined here and there. Any feedback on how to improve the program and make it run faster is appreciated :)

• Our method is based on relevance vector machine (RVM), I used the SparseBayes software version 2.0, provided by Tipping himself. I had to modify the code in SB2_FullStatistics.m at line 105 to make sure that matrix A is positive definite. I used nearestSPD.m to get the nearest PD matrix.

• You can find our implementation of MWAdp and MWAdpJITL methods on debutanizer column data in Demo.m file.

20 simulations runs were conducted for 8 different concept drift models (CDM). Details of the simulation models can be found in the article. Each CDM is stored as a struct with "X" and "Y" arrays. For a total of 700 observations, 19 predictors and their 2 lagged measurements are included in X (there are 19x3 = 57 predictors/columns in total in X) and the response variables (concentration of product B) is in Y.