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Open problems in Forecasting time

Event-driven time series forecasting1 paper raises it · 2 address it
Real-world time series are often influenced by exogenous events, which conventional models ignore.
- SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecastingraises · addresses
- TimeBraid: Unifying Time Series and Language for Understanding and Forecastingaddresses
Trade-off between accuracy and distributional realism in forecasting1 paper raises it · 2 address it
Models often excel at either prediction accuracy or distributional realism, but not both.
- Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspectiveraises · addresses
- PathFM: Fast Generative Forecasting of Multivariate Trajectoriesaddresses
Dependence structure modeling in multivariate forecasting1 paper raises it · 1 addresses it
Most models predict marginal quantiles, ignoring the dependence structure of future trajectories.
- PathFM: Fast Generative Forecasting of Multivariate Trajectoriesraises · addresses
Interpretability in sequential event prediction1 paper raises it · 1 addresses it
Existing methods prioritize accuracy over explainability, which is critical in many applications.
- What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rulesraises · addresses
Zero-shot forecasting for domain-specific data1 paper raises it · 1 addresses it
Foundation models struggle with zero-shot performance in specialized domains like soil moisture forecasting.
- Zero-Shot Forecasting of In-Situ Soil Moisture and Temperature with a Time-Series Foundation Modelraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Topics in this field

forecasting time · forecasters · forecasting203 papers26 this week
weather forecasting · weather prediction · forecasts166 papers20 this week
financial forecasting · based trading · price forecasting79 papers14 this week
data assimilation · forecast · probabilistic forecasting66 papers6 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.LGSep 27
Enhanced Trading Systems with Intelligent Market Analysis
Vedanth Padmaraman, Shreen Anand, Vamshi Vishruth J, Vineet V Pai, Arvind Srinath K. T +2
This paper proposes a hybrid reinforcement learning trading framework integrating intraday market data with transformer-based financial sentiment signals. The system extracts sentiment dynamics and semantic event clusters from financial news and incorporates them into a PPO-based trading agent. Experiments on intraday NSE data demonstrate improved risk-adjusted returns compared to price-only baselines.
PDF arXiv
arXiv cs.CLSep 27
CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting
Ya-Wen Wu, Meng-Fen Chiang, Kuang-Da Wang, Wen-Chih Peng
Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrative disclosures. LLMs can distill textual evidence but can produce scale-misaligned forecasts, whereas history-based anchors are stable but miss forecast-time signals such as product transitions, supply constraints, and management guidance. We introduce CAME (Company-Aware Evidence-Memory Experts), a residual-forecasting framework that refines a no-leakage statistical anchor when current semantic evidence and prior error patterns justify an adjustment. On a development-inclusive rolling backtest of 336 company-quarters from 12 large public technology and platform firms, CAME achieves the lowest aggregate point-estimate error among the reported methods, with statistically supported macro-sMAPE gains over the matched Statistical Anchor, and outperforms History + Guidance on all six aggregate metrics. CAME also links adjustments to source-linked evidence cards and guarded memory traces, supporting forecast inspection, provenance, and failure localization.
PDF arXiv
arXiv cs.LGSep 27
Simulation-Free Learning of GP-SDEs from Irregular Observations
Zhidi Lin, Yuhao Liu, Ying Li, Edwin Fong, Petar Djurić
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learning and inference from noisy and irregular observations remain computationally challenging. To address this issue, we propose GP-SDE Matching, a simulation-free variational framework for Bayesian GP drift learning and continuous-time state smoothing. We analytically marginalize the sparse GP posterior to derive a tractable drift-matching objective that accounts for both the posterior mean and uncertainty of the unknown drift. To handle irregular observations, we further introduce an irregular-time-aware variational state posterior that incorporates the actual observation times during both encoding and continuous-time marginal querying. Experiments on the stochastic Lorenz--63 system demonstrate substantially improved drift recovery and state reconstruction under irregular observations, while five system identification benchmarks show robust forecasting under increasing observation sparsity and competitive performance against existing latent-SDE and state-space methods.
PDF arXiv
arXiv physics.ao-phSep 27
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan, Dongjin Song, Jingchao Ni
Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases in physics-based models. Physical models, while widely used, rely on approximations and parameterizations that limit their accuracy under complex ocean-atmosphere conditions. To enhance ocean wave forecasting, we propose BridgeCast, within a physics-AI hybrid framework for bias correction. BridgeCast is a probabilistic model based on conditional flow matching (CFM) that learns to transform physical model forecasts into reanalysis-like fields. It treats physical forecasts as corrupted observations and employs a continuous-time generative process to bridge their distribution toward that of reanalysis data. BridgeCast is parameterized by a Transformer-based architecture that enables spatiotemporal modeling, incorporation of exogenous atmospheric variables, and flexible inference via both ordinary and stochastic differential equation formulations. Extensive experiments on real-world datasets demonstrate that BridgeCast consistently outperforms state-of-the-art baselines across regions and forecast lead times.
PDF arXiv
arXiv cs.LGSep 27
DevelopmentODE: Structured Neural ODEs for Early Brain Development Dynamics Across a Decade
Kaiqiao Han, Haitao Chen, Bryan Quah, Xiaoda Wang, Janelle Liu +3
Understanding how individual brain development unfolds over childhood requires modeling developmental trajectories from sparse longitudinal observations. Long-term neurodevelopmental forecasting is challenging because each child is typically observed at only a few irregularly spaced visits, while developmental dynamics vary across individuals and age. Generic continuous-time models accommodate irregular timing but often absorb these factors into a single flexible transition function, providing little structure for how population progression, individual variability, and developmental age shape the dynamics. We propose DevelopmentODE, a structured continuous-time framework that organizes population- and subject-specific variation within a shared developmental geometry while allowing the governing dynamics to evolve with age. The model builds this geometry around a developmental canal representing the population trajectory, whose local direction provides a reference for organizing subject-specific variation. Subject deviation velocities are constrained relative to this direction, while a shared nonlinear deviation field captures individual developmental motion without disrupting…
PDF arXiv
arXiv physics.ao-phSep 26
Global Evaluation of AI and NWP Precipitation Forecasts During Atmospheric River Events
Marina Vicens-Miquel, Taylor Mandelbaum, Amy McGovern, Aaron J. Hill, Daniel Rothenberg
Atmospheric rivers (ARs) produce many of the world's most extreme precipitation events and hydrometeorological hazards. Although artificial intelligence weather prediction (AIWP) models have demonstrated skill comparable to or exceeding numerical weather prediction (NWP) systems for large-scale atmospheric variables, their ability to forecast AR-related precipitation remains insufficiently characterized globally. Here, we evaluate 24-hour precipitation forecasts from the Global Forecast System (GFS), Global Ensemble Forecast System (GEFS), GraphCast, and Artificial Intelligence Forecasting System (AIFS) from Day 1 through Day 10 globally and across North America, Europe, and Australia and New Zealand. Using the Extreme Weather Bench framework, forecasts are evaluated against Integrated Multi-satellitE Retrievals for GPM (IMERG) observations using measures of precipitation magnitude, spatial structure, and localization. GraphCast and AIFS exhibit greater spatial skill than GFS and GEFS, particularly for heavy precipitation and at longer lead times, and better preserve the spatial organization of AR-related precipitation through Day 10. However, this improved spatial skill does not…
PDF arXiv

Papers

Topics in this field

forecasting time · forecasters · forecasting203 papers26 this week
weather forecasting · weather prediction · forecasts166 papers20 this week
financial forecasting · based trading · price forecasting79 papers14 this week
data assimilation · forecast · probabilistic forecasting66 papers6 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.LGSep 27
Enhanced Trading Systems with Intelligent Market Analysis
Vedanth Padmaraman, Shreen Anand, Vamshi Vishruth J, Vineet V Pai, Arvind Srinath K. T +2
This paper proposes a hybrid reinforcement learning trading framework integrating intraday market data with transformer-based financial sentiment signals. The system extracts sentiment dynamics and semantic event clusters from financial news and incorporates them into a PPO-based trading agent. Experiments on intraday NSE data demonstrate improved risk-adjusted returns compared to price-only baselines.
PDF arXiv
arXiv cs.CLSep 27
CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting
Ya-Wen Wu, Meng-Fen Chiang, Kuang-Da Wang, Wen-Chih Peng
Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrative disclosures. LLMs can distill textual evidence but can produce scale-misaligned forecasts, whereas history-based anchors are stable but miss forecast-time signals such as product transitions, supply constraints, and management guidance. We introduce CAME (Company-Aware Evidence-Memory Experts), a residual-forecasting framework that refines a no-leakage statistical anchor when current semantic evidence and prior error patterns justify an adjustment. On a development-inclusive rolling backtest of 336 company-quarters from 12 large public technology and platform firms, CAME achieves the lowest aggregate point-estimate error among the reported methods, with statistically supported macro-sMAPE gains over the matched Statistical Anchor, and outperforms History + Guidance on all six aggregate metrics. CAME also links adjustments to source-linked evidence cards and guarded memory traces, supporting forecast inspection, provenance, and failure localization.
PDF arXiv
arXiv cs.LGSep 27
Simulation-Free Learning of GP-SDEs from Irregular Observations
Zhidi Lin, Yuhao Liu, Ying Li, Edwin Fong, Petar Djurić
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learning and inference from noisy and irregular observations remain computationally challenging. To address this issue, we propose GP-SDE Matching, a simulation-free variational framework for Bayesian GP drift learning and continuous-time state smoothing. We analytically marginalize the sparse GP posterior to derive a tractable drift-matching objective that accounts for both the posterior mean and uncertainty of the unknown drift. To handle irregular observations, we further introduce an irregular-time-aware variational state posterior that incorporates the actual observation times during both encoding and continuous-time marginal querying. Experiments on the stochastic Lorenz--63 system demonstrate substantially improved drift recovery and state reconstruction under irregular observations, while five system identification benchmarks show robust forecasting under increasing observation sparsity and competitive performance against existing latent-SDE and state-space methods.
PDF arXiv
arXiv physics.ao-phSep 27
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan, Dongjin Song, Jingchao Ni
Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases in physics-based models. Physical models, while widely used, rely on approximations and parameterizations that limit their accuracy under complex ocean-atmosphere conditions. To enhance ocean wave forecasting, we propose BridgeCast, within a physics-AI hybrid framework for bias correction. BridgeCast is a probabilistic model based on conditional flow matching (CFM) that learns to transform physical model forecasts into reanalysis-like fields. It treats physical forecasts as corrupted observations and employs a continuous-time generative process to bridge their distribution toward that of reanalysis data. BridgeCast is parameterized by a Transformer-based architecture that enables spatiotemporal modeling, incorporation of exogenous atmospheric variables, and flexible inference via both ordinary and stochastic differential equation formulations. Extensive experiments on real-world datasets demonstrate that BridgeCast consistently outperforms state-of-the-art baselines across regions and forecast lead times.
PDF arXiv
arXiv cs.LGSep 27
DevelopmentODE: Structured Neural ODEs for Early Brain Development Dynamics Across a Decade
Kaiqiao Han, Haitao Chen, Bryan Quah, Xiaoda Wang, Janelle Liu +3
Understanding how individual brain development unfolds over childhood requires modeling developmental trajectories from sparse longitudinal observations. Long-term neurodevelopmental forecasting is challenging because each child is typically observed at only a few irregularly spaced visits, while developmental dynamics vary across individuals and age. Generic continuous-time models accommodate irregular timing but often absorb these factors into a single flexible transition function, providing little structure for how population progression, individual variability, and developmental age shape the dynamics. We propose DevelopmentODE, a structured continuous-time framework that organizes population- and subject-specific variation within a shared developmental geometry while allowing the governing dynamics to evolve with age. The model builds this geometry around a developmental canal representing the population trajectory, whose local direction provides a reference for organizing subject-specific variation. Subject deviation velocities are constrained relative to this direction, while a shared nonlinear deviation field captures individual developmental motion without disrupting…
PDF arXiv
arXiv physics.ao-phSep 26
Global Evaluation of AI and NWP Precipitation Forecasts During Atmospheric River Events
Marina Vicens-Miquel, Taylor Mandelbaum, Amy McGovern, Aaron J. Hill, Daniel Rothenberg
Atmospheric rivers (ARs) produce many of the world's most extreme precipitation events and hydrometeorological hazards. Although artificial intelligence weather prediction (AIWP) models have demonstrated skill comparable to or exceeding numerical weather prediction (NWP) systems for large-scale atmospheric variables, their ability to forecast AR-related precipitation remains insufficiently characterized globally. Here, we evaluate 24-hour precipitation forecasts from the Global Forecast System (GFS), Global Ensemble Forecast System (GEFS), GraphCast, and Artificial Intelligence Forecasting System (AIFS) from Day 1 through Day 10 globally and across North America, Europe, and Australia and New Zealand. Using the Extreme Weather Bench framework, forecasts are evaluated against Integrated Multi-satellitE Retrievals for GPM (IMERG) observations using measures of precipitation magnitude, spatial structure, and localization. GraphCast and AIFS exhibit greater spatial skill than GFS and GEFS, particularly for heavy precipitation and at longer lead times, and better preserve the spatial organization of AR-related precipitation through Day 10. However, this improved spatial skill does not…
PDF arXiv

Activity · 12 weeks

Open problems in Forecasting time

Event-driven time series forecasting1 paper raises it · 2 address it
Real-world time series are often influenced by exogenous events, which conventional models ignore.
- SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecastingraises · addresses
- TimeBraid: Unifying Time Series and Language for Understanding and Forecastingaddresses
Trade-off between accuracy and distributional realism in forecasting1 paper raises it · 2 address it
Models often excel at either prediction accuracy or distributional realism, but not both.
- Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspectiveraises · addresses
- PathFM: Fast Generative Forecasting of Multivariate Trajectoriesaddresses
Dependence structure modeling in multivariate forecasting1 paper raises it · 1 addresses it
Most models predict marginal quantiles, ignoring the dependence structure of future trajectories.
- PathFM: Fast Generative Forecasting of Multivariate Trajectoriesraises · addresses
Interpretability in sequential event prediction1 paper raises it · 1 addresses it
Existing methods prioritize accuracy over explainability, which is critical in many applications.
- What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rulesraises · addresses
Zero-shot forecasting for domain-specific data1 paper raises it · 1 addresses it
Foundation models struggle with zero-shot performance in specialized domains like soil moisture forecasting.
- Zero-Shot Forecasting of In-Situ Soil Moisture and Temperature with a Time-Series Foundation Modelraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 24m ago

Forecasting time

Found by the topic model: forecasting time, weather forecasting, financial forecasting.

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