A Natural Gas Consumption Forecasting System for Continual Learning
Scenarios based on Hoeffding Trees with Change Point Detection Mechanism
- URL: http://arxiv.org/abs/2309.03720v3
- Date: Mon, 4 Mar 2024 13:52:35 GMT
- Title: A Natural Gas Consumption Forecasting System for Continual Learning
Scenarios based on Hoeffding Trees with Change Point Detection Mechanism
- Authors: Radek Svoboda, Sebastian Basterrech, J\k{e}drzej Kozal, Jan
Plato\v{s}, Micha{\l} Wo\'zniak
- Abstract summary: This article introduces a novel multistep ahead forecasting of natural gas consumption with change point detection integration.
The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case.
- Score: 1.3999481573773074
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Forecasting natural gas consumption, considering seasonality and trends, is
crucial in planning its supply and consumption and optimizing the cost of
obtaining it, mainly by industrial entities. However, in times of threats to
its supply, it is also a critical element that guarantees the supply of this
raw material to meet individual consumers' needs, ensuring society's energy
security. This article introduces a novel multistep ahead forecasting of
natural gas consumption with change point detection integration for model
collection selection with continual learning capabilities using data stream
processing. The performance of the forecasting models based on the proposed
approach is evaluated in a complex real-world use case of natural gas
consumption forecasting. We employed Hoeffding tree predictors as forecasting
models and the Pruned Exact Linear Time (PELT) algorithm for the change point
detection procedure. The change point detection integration enables selecting a
different model collection for successive time frames. Thus, three model
collection selection procedures (with and without an error feedback loop) are
defined and evaluated for forecasting scenarios with various densities of
detected change points. These models were compared with change point agnostic
baseline approaches. Our experiments show that fewer change points result in a
lower forecasting error regardless of the model collection selection procedure
employed. Also, simpler model collection selection procedures omitting
forecasting error feedback leads to more robust forecasting models suitable for
continual learning tasks.
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