Thanapol Phungtua-eng

arXiv2026

LGTD: Local-Global Trend Decomposition for Season-Length-Free Time Series Analysis

Time series decomposition into trend, seasonal, and residual components is a fundamental primitive in data mining and analytics pipelines, underpinning anomaly detection, change-point analysis, and forecasting. Most existing methods require a user-specified or estimated season length and assume stable periodic structure. In large, heterogeneous collections, where recurring patterns drift, appear intermittently, or operate at multiple nonstationary scales, period selection becomes brittle and per-series tuning does not scale. We propose LGTD (Local-Global Trend Decomposition), a season-length-free decomposition framework that requires no period specification and operates with a single fixed default configuration across datasets. LGTD represents a series as the sum of (i) a smooth global trend capturing long-term evolution, (ii) adaptive local trends inferred by an error-driven local linear segmentation procedure, and (iii) a residual component. Rather than modeling seasonality through an explicit periodic basis, LGTD treats it as an emergent property arising from the recurrence of local trend regimes, decoupling decomposition quality from any estimated season length. We prove that the local trend inference procedure terminates in a bounded number of iterations and runs in linear time in the series length, independent of any seasonal parameter, and confirm this empirically: LGTD scales linearly in runtime and memory and is the fastest method across all tested lengths, while several baselines degrade super-linearly. On synthetic benchmarks LGTD achieves balanced accuracy across fixed, transitive, and variable season-length regimes, particularly where period-based methods degrade, and on real-world data it yields interpretable components and low-structure residuals. Source code and datasets are available at https://github.com/chotanansub/LGTD.

cs.DB↗

arXiv2026

Are We Winning the Wrong Game? Revisiting Evaluation Practices for Long-Term Time Series Forecasting

Long-term time series forecasting (LTSF) is widely recognized as a central challenge in data mining and machine learning. LTSF has increasingly evolved into a benchmark-driven ''GAME,'' where models are ranked, compared, and declared state-of-the-art based primarily on marginal reductions in aggregated pointwise error metrics such as MSE and MAE. Across a small set of canonical datasets and fixed forecasting horizons, progress is communicated through leaderboard-style tables in which lower numerical scores define success. In this GAME, what is measured becomes what is optimized, and incremental error reduction becomes the dominant currency of advancement. We argue that this metric-centric regime is not merely incomplete, but structurally misaligned with the broader objectives of forecasting. In real-world settings, forecasting often prioritizes preserving temporal structure, trend stability, seasonal coherence, robustness to regime shifts, and supporting downstream decision processes. Optimizing aggregate pointwise error does not necessarily imply modeling these structural properties. As a result, leaderboard improvement may increasingly reflect specialization in benchmark configurations rather than a deeper understanding of temporal dynamics. This paper revisits LTSF evaluation as a foundational question in data science: what does it mean to measure forecasting progress? We propose a multi-dimensional evaluation perspective that integrates statistical fidelity, structural coherence, and decision-level relevance. By challenging the current metric monoculture, we aim to redirect attention from winning benchmark tables toward advancing meaningful, context-aware forecasting.

cs.LG↗