MODEL SELECTION OF ENSEMBLE FORECASTING USING WEIGHTED SIMILARITY OF TIME SERIES
Abstract: Several methods have
been proposed to combine the forecasting results into single forecast namely
the simple averaging, weighted average on validation performance, or
non-parametric combination schemas. These methods use fixed combination of
individual forecast to get the final forecast result. In this paper, quite
different approach is employed to select the forecasting methods, in which
every point to forecast is calculated by using the best methods used by similar
training dataset. Thus, the selected methods may differ at each point to
forecast. The similarity measures used to compare the time series for testing
and validation are Euclidean and Dynamic Time Warping (DTW), where each point
to compare is weighted according to its recentness. The dataset used in the
experiment is the time series data designated for NN3 Competition and time
series generated from the frequency of USPTO’s patents and PubMed’s
scientific publications on the field of health, namely on Apnea, Arrhythmia,
and Sleep Stages. The experimental result shows that the weighted combination
of methods selected based on the similarity between training and testing data
may perform better compared to either the unweighted combination of methods
selected based on the similarity measure or the fixed combination of best
individual forecast.
Keywords: ensemble
forecasting; kesamaan tertimbang; model selection; perkiraan ansambel; seleksi
model; time series; weighted similarity
Author: Agus Widodo, Indra
Budi
Journal Code: jptkomputergg120010
