Abstract:
Motivated by the problem of finding optimal Performance vs. Complexity trade-off in the task of forecasting time series data, we propose a model-agnostic method MetaSieve...Show MoreMetadata
Abstract:
Motivated by the problem of finding optimal Performance vs. Complexity trade-off in the task of forecasting time series data, we propose a model-agnostic method MetaSieve that performs data dichotomy (i.e., in fact, sieves the data instances in a meta-learning manner) according to a chosen quality level while iterating over the model's complexity. The method is inspired by classical iterative numerical optimization ones but is applied to sets of time series. As a result, the method is significantly less time consuming than a traditional brute force-based meta-learning algorithm. It further turns out in the experiments that the MetaSieve quality results are rather comparable to those of the brute force-based one thus one has a noticeable reduction in time consumption in exchange for a slight decrease of forecasting quality. Additionally, we experimentally show a good performance of a MetaSieve-based classifier that provides the Performance vs. Complexity classes a priori, i.e. before the actual forecasting, on synthetic and real-world time series data.
Date of Conference: 28 November 2022 - 01 December 2022
Date Added to IEEE Xplore: 08 February 2023
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ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation
ITMO University, Saint Petersburg, Russian Federation