Papers Time Series Forecasting
“Time Series Forecasting” 태그가 달린 논문 2,083편 · 필터 해제
SEER: Self-Evolving Event Reasoning and Retrieval for 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…
Time Series ForecastingPooling Helps, Learned Weighting Hurts In-Context: Decomposing Group Attention
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 ForecastingNanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization
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 ForecastingPRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting
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-tuningTime Series ForecastingMZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting
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 ForecastingLearning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting
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 ForecastingSAGE: Variate-Wise Semantic Augmentation for Vision-Language Time Series Forecasting
Time series forecasting models operate on raw numerical sequences, lacking the semantic knowledge that domain experts implicitly leverage, such as the physical meaning of each variable, its statistical behavior, and its …
Time Series ForecastingCEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as budget schedules, rather than passively pre…
Time Series ForecastingModeling spatio-temporal locality in multi-step forecasting of geo-referenced time series
Forecasting future measurements from geographically distributed sensors is essential across many domains. However, the spatial distribution of these sensors raises multiple challenges, primarily due to spatial autocorrel…
Time Series ForecastingMetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact, specialized forecasters are more desira…
Computational EfficiencyTime Series ForecastingFew-Shot LearningWhich Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision
Historical retrieval for time-series prediction commonly treats past similarity as a proxy for usefulness. We ask a different question: which historical examples should be expected to matter for a query? We define predic…
Time Series ForecastingBeyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting
Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficiently studied. Existing benchmarks typically use mean squared error (MSE) as the eval…
Time Series ForecastingRethinking Irregular Time Series Forecasting from the Perspective of Basis Functions
Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characteristics of irregular time series, including sparse observations and no…
Time Series ForecastingWhen Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version
Diffusion models offer a natural way to model uncertainty in time series forecasting, yet their iterative sampling process is often treated as a uniformly beneficial refinement procedure. Our study challenges this view b…
Time Series ForecastingModel-agnostic Retrieval-Augmented Extended Forecasting for time series
Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities. However, achieving optimal performance on time series with short or negligible historical data in domain-specific …
Time Series ForecastingDomain AdaptationA Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs. We…
Time Series ForecastingTS-RAG: Retrieval Augmented Generation for Time Series Forecasting
While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limit…
Time Series ForecastingPOEM: Phase-Aware $\mathrm{SO}(2)$ Feature Rotation for Time Series Forecasting Under Periodicity Drift
Deep learning has advanced time series forecasting, but periodicity drift, in which cycle timing and phase vary over time, remains a challenging problem. Existing methods predominantly model these sequences on fixed time…
Time Series ForecastingInformation Bottleneck Learning for Faithful Time Series Forecasting Explanations
As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial as the predictions themselves. Although…
Time Series ForecastingEnhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework
Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenges stem from asynchronous observations, n…
Human Activity RecognitionTime Series Forecasting