Time Series Foundational Models: Their Role in Anomaly Detection and Prediction

2024-12-26

AI Summary

This study systematically evaluates the practical efficacy of time series foundation models (TSFMs) on anomaly detection and forecasting tasks, specifically targeting multivariate irregular time series lacking prominent trends, seasonality, or regular patterns. Adopting a unified preprocessing and evaluation protocol, we benchmark TSFMs—including TimesNet, DLinear, and others—against classical baselines (ARIMA, Isolation Forest, LSTM, Transformer). Results reveal that TSFMs do not consistently outperform lightweight traditional models across major benchmarks; they suffer from fundamental limitations including poor interpretability, weak few-shot/zero-shot generalization, and inadequate long-range dependency modeling—while incurring substantially higher computational costs. To our knowledge, this is the first work to empirically identify performance ceilings of TSFMs for such irregular time series tasks. We publicly release all datasets, code, and analytical artifacts to establish an evidence-based benchmark and methodological reference for rational deployment and future advancement of TSFMs.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their application in anomaly detection and prediction remains underexplored, with growing concerns regarding their black-box nature, lack of interpretability and applicability. This paper critically evaluates the efficacy of TSFM in anomaly detection and prediction tasks. We systematically analyze TSFM across multiple datasets, including those characterized by the absence of discernible patterns, trends and seasonality. Our analysis shows that while TSFMs can be extended for anomaly detection and prediction, traditional statistical and deep learning models often match or outperform TSFM in these tasks. Additionally, TSFMs require high computational resources but fail to capture sequential dependencies effectively or improve performance in few-shot or zero-shot scenarios. oindent The preprocessed datasets, codes to reproduce the results and supplementary materials are available at https://github.com/smtmnfg/TSFM.

Problem

• Time Series Foundation Model
• Anomaly Detection
• Performance Comparison

Innovation

• Time Series Foundation Model
• Anomaly Detection
• Resource Consumption Analysis