A generic multilevel architecture for time series prediction

Authors: Ruta, D., Gabrys, B. and Lemke, C.

Journal: IEEE Transactions on Knowledge and Data Engineering

Volume: 23

Issue: 3

Pages: 350-359

ISSN: 1041-4347

DOI: 10.1109/TKDE.2010.137

Abstract:

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and cause a demand for both univariate and multivariate time series forecasting. For such data, traditional predictive models based on autoregression are often not sufficient to capture complex nonlinear relationships between multidimensional features and the time series outputs. In order to exploit these relationships for improved time series forecasting while also better dealing with a wider variety of prediction scenarios, a forecasting system requires a flexible and generic architecture to accommodate and tune various individual predictors as well as combination methods. In reply to this challenge, an architecture for combined, multilevel time series prediction is proposed, which is suitable for many different universal regressors and combination methods. The key strength of this architecture is its ability to build a diversified ensemble of individual predictors that form an input to a multilevel selection and fusion process before the final optimized output is obtained. Excellent generalization ability is achieved due to the highly boosted complementarity of individual models further enforced through cross-validation-linked training on exclusive data subsets and ensemble output postprocessing. In a sample configuration with basic neural network predictors and a mean combiner, the proposed system has been evaluated in different scenarios and showed a clear prediction performance gain. © 2006 IEEE.

Source: Scopus

A Generic Multilevel Architecture for Time Series Prediction

Authors: Ruta, D., Gabrys, B. and Lemke, C.

Journal: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Volume: 23

Issue: 3

Pages: 350-359

eISSN: 1558-2191

ISSN: 1041-4347

DOI: 10.1109/TKDE.2010.137

Source: Web of Science (Lite)

A Generic Multilevel Architecture for Time Series Prediction

Authors: Ruta, D., Gabrys, B. and Lemke, C.

Journal: IEEE Transactions on Knowledge and Data Engineering

Volume: 99

ISSN: 1041-4347

DOI: 10.1109/TKDE.2010.137

Abstract:

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and cause a demand for both univariate and multivariate time series forecasting. For such data, traditional predictive models based on autoregression are often not sufficient to capture complex non-linear relationships between multidimensional features and the time series outputs. In order to exploit these relationships for improved time series forecasting while also better dealing with a wider variety of prediction scenarios, a forecasting system requires a flexible and generic architecture to accommodate and tune various individual predictors as well as combination methods.

In reply to this challenge, an architecture for combined, multilevel time series prediction is proposed, which is suitable for many different universal regressors and combination methods. The key strength of this architecture is its ability to build a diversified ensemble of individual predictors that form the input to a multilevel selection and fusion process before the final optimised output is obtained. Excellent generalisation ability is achieved due to the highly boosted complementarity of individual models further enforced through crossvalidation-linked training on exclusive data subsets and ensemble output post-processing. In a sample configuration with basic neural network predictors and a mean combiner, the proposed system has been evaluated in different scenarios and showed a clear prediction performance gain.

Source: Manual

Preferred by: Dymitr Ruta

A Generic Multilevel Architecture for Time Series Prediction.

Authors: Ruta, D., Gabrys, B. and Lemke, C.

Journal: IEEE Trans. Knowl. Data Eng.

Volume: 23

Pages: 350-359

DOI: 10.1109/TKDE.2010.137

Source: DBLP