A fast projection algorithm for sequence data searching
Data & Knowledge Engineering - Special issue: next generation information technologies and systems
Machine Learning
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Local Learning for Iterated Time-Series Prediction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Providing wastewater treatment plants with predictive knowledge based on transition networks
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Variable selection for wind power prediction using particle swarm optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Anomaly detection in streaming environmental sensor data: A data-driven modeling approach
Environmental Modelling & Software
Temporal data mining for smart homes
Designing Smart Homes
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This paper presents an application of lazy learning algorithms in the domain of industrial processes. These processes are described by a set of variables, each corresponding a time series. Each variable plays a different role in the process and some mutual influences can be discovered.A methodology to study the different variables and their roles in the process are described. This methodology allows the structuration of the study of the time series.The prediction methodology is based on a k-nearest neighbour algorithm. A complete study of the different parameters of this kind of algorithm is done, including data preprocessing, neighbour distance, and weighting strategies. An alternative to Euclidean distance called shape distance is presented, this distance is insensitive to scaling and translation. Alternative weighting strategies based on time series autocorrelation and partial autocorrelation are also presented.Experiments using autorregresive models, simulated data and real data obtained from an industrial process (Waste water treatment plants) are presented to show the feasabilty of our approach.