Rethinking Multimodal Time-Series Forecasting Evaluation

2024-09-01 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

TimesX is a new context-enriched, multimodal time series forecasting benchmark designed to address limitations in existing evaluation datasets. It features real-world, large-scale, cross-domain data, encompassing 19 diverse domains with 190 variables, covering daily and weekly frequencies from 2018 to 2025. A key innovation is its automated data generation pipeline, which continuously mitigates data leakage and ensures comprehensive, high-quality textual contexts. Empirical studies on TimesX, involving over 312,000 LLM inferences, reveal that many approaches performing well on synthetic benchmarks may fail on TimesX. Notably, simple ensemble methods leveraging rich textual context outperform complex agentic solutions and strong baselines, while existing benchmarks can over- or underestimate LLM performance and context importance.

Key takeaway

For Machine Learning Engineers evaluating multimodal time-series forecasting models, recognize that synthetic benchmarks can mislead performance assessments. Focus on real-world, leakage-free benchmarks like TimesX to ensure valid comparisons. Consider simple ensemble approaches, which surprisingly outperform complex agentic LLM solutions. Ensure your models effectively integrate diverse textual contexts, as this significantly improves accuracy and uncertainty modeling.

Key insights

Robust multimodal time-series forecasting benchmarks require real-world data, leakage mitigation, and diverse, high-quality textual contexts for reliable model evaluation.

Principles

• Synthetic benchmarks can severely over-estimate LLM performance and bias model rankings.
• Data leakage from pretraining datasets significantly inflates LLM forecasting accuracy.
• Diverse textual contexts (metadata, calendar, covariates, events) are crucial for multimodal TSF accuracy.

Method

TimesX employs a multi-agent automated pipeline (Hypothesizer, Verifier, Enricher, Synthesizer) for continuous, leakage-free generation and enrichment of real-world textual event contexts, ensuring timestamp accuracy and factual correctness.

In practice

• Prioritize simple ensemble methods over complex agentic LLM solutions for multimodal TSF, as they perform better on real-world data.
• Integrate all available context types (metadata, calendar, covariates, time-stamped events) for compounded accuracy gains.

Topics

• Multimodal Time-Series Forecasting
• Benchmark Datasets
• Data Leakage Mitigation
• Large Language Models
• Time-Series Foundation Models
• Ensemble Learning
• Contextual AI

Code references

• decisionintelligence/TFB

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.