Category Archives: hierarchical clustering

Calculating Derivatives from Random Forests

(Comment on prediction intervals for random forests, and links to a paper.) (Edits to repair smudges, 2020-06-28, about 0945 EDT. Closing comment, 2020-06-30, 1450 EDT.) There are lots of ways of learning about mathematical constructs, even about actual machines. One … Continue reading

Posted in bridge to somewhere, Calculus, dependent data, dynamic generalized linear models, dynamical systems, ensemble methods, ensemble models, filtering, forecasting, hierarchical clustering, linear regression, model-free forecasting, Monte Carlo Statistical Methods, non-mechanistic modeling, non-parametric model, non-parametric statistics, numerical algorithms, prediction, R statistical programming language, random forests, regression, sampling, splines, statistical learning, statistical series, statistics, time derivatives, time series | Leave a comment

A look at an electricity consumption series using SNCDs for clustering

(Slightly amended with code and data link, 12th January 2019.) Prediction of electrical load demand or, in other words, electrical energy consumption is important for the proper operation of electrical grids, at all scales. RTOs and ISOs forecast demand based … Continue reading

Posted in American Statistical Association, consumption, data streams, decentralized electric power generation, dendrogram, divergence measures, efficiency, electricity, electricity markets, energy efficiency, energy utilities, ensembles, evidence, forecasting, grid defection, hierarchical clustering, hydrology, ILSR, information theoretic statistics, local self reliance, Massachusetts, microgrids, NCD, normalized compression divergence, numerical software, open data, prediction, rate of return regulation, Sankey diagram, SNCD, statistical dependence, statistical series, statistics, sustainability, symmetric normalized compression divergence, time series | 1 Comment