Time Series Forecasting

Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retriev…

Pooling Helps, Learned Weighting Hurts In-Context: Decomposing Group Attention

2026-10-01 · Michael Fore, James Mason Inder, Mrishika Nair, Praneetha Vaddamanu 외 arxiv

Group attention, introduced by the time series forecasting model Chronos-2, attends over the variates of a group at a fixed patch index and serves both multivariate (MV) and in-context learning (ICL) forecasting. Rather …

Time Series Forecasting

NanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization

2026-09-15 · Gautam Kishore hf

We present NanoForecast v0.5, a 6.5M-parameter forecaster that competes with models 31x its size (TimesFM, 200M parameters) after training pipeline fixes and no architecture change. Retraining the v0.3 architecture with …

Time Series Forecasting

PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting

2026-09-04 · Maryam Fakhari, Mehran Safayani arxiv

Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional forecasting methods. Although Large Language Models (LLMs) have shown promise for time series forecasting, the com…

parameter-efficient fine-tuning Time Series Forecasting

MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting

2026-09-04 · Yifang Zhang, Shengwu Xiong, Henan Wang, Wenjie Yin 외 arxiv

Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and disaster prevention, which typically is formulated as a time series forecasting problem. However, conventional t…

Time Series Forecasting

Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting

2026-08-28 · Morad Laglil, Younes Hlal, Marouane El Hadari, Emilie Devijver 외 arxiv

Reliable long-horizon time series forecasting is an important yet difficult problem. Trends and seasonality introduce complex temporal structure that challenges learning-based forecasting models. Differencing, which subt…

Time Series Forecasting

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