Nearest-neighbours for time series
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.
lazy learning distance functions feature weighting