Filtering in Preprocessing for Anomaly Detection in Time Series - de Araujo, Ricardo Fernandes; Baroni, Laís; Bezerra, Eduardo; Guedes, Gustavo; Pacitti, Esther; Pinheiro, Larrisa; Salles, Diego; Porto, Fabio; Ogasawara, Eduardo (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Filtering is often adopted as a preprocessing step in time series pipelines, although its impact on anomaly detection remains unclear. We evaluate 14 filters with an ARIMA-based detector on the GECCO Industrial Challenge 2018 and Yahoo! Webscope S5 benchmarks. Filtering improves F1-Score in only 25.36% of the runs; in the remaining 74.64%, it either fails to improve performance or degrades it. The results reveal a trade-off between stability and potential gains. Adaptive filters are more consistent, whereas decomposition-based filters can yield larger gains but also more frequent degradation. Within this ARIMA-based protocol, filtering does not behave uniformly across series and should be treated as a context-dependent engineering choice.