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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.

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Topics in this field

forecasting time · forecasters · forecasting203 papers26 this week
weather forecasting · weather prediction · forecasts166 papers21 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

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Recent papers

All papers in Research

arXiv physics.ao-phSep 28
MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation
Ning Wang, Zuliang Fang, Weixin Jin, Zhongjian Lv, Shuang Qin +17
Extending reliable nowcasting of extreme precipitation could provide critical additional time for warnings and emergency response during high-impact events such as flash floods. Radar-based generative machine-learning models have enabled skilful hyperlocal precipitation nowcasting, but accurate prediction of intense precipitation remains confined to the first few hours. Because storm-scale structure is predictable for longer than individual cells, a natural strategy is to predict that structure while generatively modelling only the uncertain local growth, decay, reorganisation and initiation of storms. Here we present Microsoft Weather Nowcast (MW-Nowcast), a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor to capture organised precipitation structure shared across ensemble members, and a generator to produce diverse local residuals around this shared prediction. Across independent test data from the United States, Europe and China, MW-Nowcast achieves higher detection skill than leading methods for heavy and extreme precipitation throughout the 6 h horizon. For the most intense rainfall, MW-Nowcast doubles the available warning time across…
PDF arXiv
arXiv cs.LGSep 28
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Zehao Xiao, Shifeng Xie, Lei Zan, Jianfeng Zhang, Lujia Pan +3
Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual information can be provided more efficiently through a compact set of learned token embeddings. We introduce PaCTS, which generates a small set of instance-adaptive latent prompts in the form of continuous embedding tokens conditioned on the visible context. These prompts serve as compact context surrogates for frozen TSFMs. PaCTS constructs them from instance-specific global statistics and further refines them with segment-level temporal information, capturing both global characteristics and local temporal variations. The prompt module is jointly trained and deployed across heterogeneous time series with the frozen backbone. Extensive experiments demonstrate the effectiveness of prompts as context, consistently improving forecasting across context lengths and model architectures. With a shorter input context, PaCTS can outperform the same frozen backbone using double context while requiring substantially less inference computation. Compared with weight-space adaptation methods, PaCTS achieves stronger improvements and better…
PDF arXiv
arXiv cs.LGSep 28
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Jia Long Bao, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi Ziabari
The reliable prediction of passenger train delays is a critical component of railway management. While contemporary research frequently attempts to maximize absolute accuracy by deploying opaque deep learning architectures, the underlying data mechanics driving longitudinal predictive decay remain underexplored. Consequently, this study provides an explainable temporal robustness analysis of network-wide railway delay prediction. Focusing on the Dutch railway network, this research utilizes interpretable tree-based ensembles to integrate granular topological, environmental, and operational features. The overarching finding establishes that while feature-rich tree-based models improve simultaneous (within-month) prediction, predictive performance systematically degrades when evaluated across non-simultaneous (future) months. Furthermore, multi-horizon SHAP and dispersion analyses explicitly link this degradation to environmental feature volatility and instability within the statistical target definition. Ultimately, this study demonstrates that richer feature sets alone are insufficient to resolve long-term forecasting constraints, underscoring the necessity to transition toward…
PDF arXiv
arXiv stat.MLSep 28
Memory Prediction Excess: A Probabilistic Quantity for Predictive Gain and Memory Length in Stochastic Processes
Jiahao Jiang
A central question in the prediction of stochastic processes is the extent to which past information can improve the probability of correctly predicting the next state. We introduce the Memory Prediction Excess (MPE) to address this question quantitatively. The MPE measures the average improvement in prediction accuracy obtained by using the entire observed history relative to using only the static marginal distribution, in discrete-time finite-state processes. It is defined as the difference between the expected optimal conditional prediction accuracy and the optimal static prediction accuracy. Its basic properties are examined: the MPE is always non-negative; it admits an upper bound depending on the static accuracy, attained if and only if the future is almost surely a deterministic function of the past; and degenerate cases in which the MPE vanishes are characterized. A normalized version, taking values in the unit interval, is introduced as a dimensionless measure of predictive efficiency. A lower bound is derived by comparing predictions based on histories of different lengths, showing that the expected optimal prediction accuracy is monotone with respect to the history…
PDF arXiv
arXiv cs.LGSep 28
Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting
Yuanyuan Deng, Mykola Pechenizkiy, Songgaojun Deng
Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference time, often leveraging delayed observed data to refine predictions. In the multivariate setting, distribution shifts often exhibit cross-variate dependencies, yet existing TSF-TTA methods adapt each variate independently and ignore this cross-variate structure. Exploiting such structure motivates cross-variate interaction, but coupling variates through backbone predictions introduces direct pathways for mixing uncorrected errors across variates, a concern under the delayed supervision of TSF-TTA. We identify the \emph{interaction space} as a key design choice, and show that acting on adapter corrections that refine backbone outputs, the \emph{correction space}, rather than on the predictions themselves, avoids directly propagating backbone errors across variates. We build on this to propose \textsc{CoRe} (\textsc{Co}rrection-space Interaction \textsc{Re}finement), realizing correction-space interaction through (i) Shared-anchor Correction Refinement (SCR), which combines each variate's correction with a shared anchor through a…
PDF arXiv
arXiv cs.LGSep 28
Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations
Oskar Bohn Lassen, Joao Paulo de Souza Boger, Simon Driscoll, Stephen I. Thomson, Sebastian Schemm +2
Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly, through tests of physical consistency. These metrics assess whether predictions are accurate and satisfy selected physical requirements, but provide limited insight into whether learned trajectories reproduce the underlying dynamics. Domain experts examine such relationships through physical representations that expose relevant processes, interactions, and responses, but these analyses are often separated from typical machine-learning evaluation. We introduce a practical framework for evaluating dynamical fidelity through predictive structure in physical representation spaces. Experts define the representations, while reference trajectories determine which relationships are predictive and retained as evaluation tests. We demonstrate the approach in atmospheric forecasting using ERA5 representations of planetary-wave activity and Northern Annular Mode evolution, and evaluate Pangu-Weather, GraphCast, and FengWu. The models exhibit distinct departures from reference predictive structure that are not reflected by conventional…
PDF arXiv

Papers

Topics in this field

forecasting time · forecasters · forecasting203 papers26 this week
weather forecasting · weather prediction · forecasts166 papers21 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 physics.ao-phSep 28
MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation
Ning Wang, Zuliang Fang, Weixin Jin, Zhongjian Lv, Shuang Qin +17
Extending reliable nowcasting of extreme precipitation could provide critical additional time for warnings and emergency response during high-impact events such as flash floods. Radar-based generative machine-learning models have enabled skilful hyperlocal precipitation nowcasting, but accurate prediction of intense precipitation remains confined to the first few hours. Because storm-scale structure is predictable for longer than individual cells, a natural strategy is to predict that structure while generatively modelling only the uncertain local growth, decay, reorganisation and initiation of storms. Here we present Microsoft Weather Nowcast (MW-Nowcast), a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor to capture organised precipitation structure shared across ensemble members, and a generator to produce diverse local residuals around this shared prediction. Across independent test data from the United States, Europe and China, MW-Nowcast achieves higher detection skill than leading methods for heavy and extreme precipitation throughout the 6 h horizon. For the most intense rainfall, MW-Nowcast doubles the available warning time across…
PDF arXiv
arXiv cs.LGSep 28
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Zehao Xiao, Shifeng Xie, Lei Zan, Jianfeng Zhang, Lujia Pan +3
Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual information can be provided more efficiently through a compact set of learned token embeddings. We introduce PaCTS, which generates a small set of instance-adaptive latent prompts in the form of continuous embedding tokens conditioned on the visible context. These prompts serve as compact context surrogates for frozen TSFMs. PaCTS constructs them from instance-specific global statistics and further refines them with segment-level temporal information, capturing both global characteristics and local temporal variations. The prompt module is jointly trained and deployed across heterogeneous time series with the frozen backbone. Extensive experiments demonstrate the effectiveness of prompts as context, consistently improving forecasting across context lengths and model architectures. With a shorter input context, PaCTS can outperform the same frozen backbone using double context while requiring substantially less inference computation. Compared with weight-space adaptation methods, PaCTS achieves stronger improvements and better…
PDF arXiv
arXiv cs.LGSep 28
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Jia Long Bao, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi Ziabari
The reliable prediction of passenger train delays is a critical component of railway management. While contemporary research frequently attempts to maximize absolute accuracy by deploying opaque deep learning architectures, the underlying data mechanics driving longitudinal predictive decay remain underexplored. Consequently, this study provides an explainable temporal robustness analysis of network-wide railway delay prediction. Focusing on the Dutch railway network, this research utilizes interpretable tree-based ensembles to integrate granular topological, environmental, and operational features. The overarching finding establishes that while feature-rich tree-based models improve simultaneous (within-month) prediction, predictive performance systematically degrades when evaluated across non-simultaneous (future) months. Furthermore, multi-horizon SHAP and dispersion analyses explicitly link this degradation to environmental feature volatility and instability within the statistical target definition. Ultimately, this study demonstrates that richer feature sets alone are insufficient to resolve long-term forecasting constraints, underscoring the necessity to transition toward…
PDF arXiv
arXiv stat.MLSep 28
Memory Prediction Excess: A Probabilistic Quantity for Predictive Gain and Memory Length in Stochastic Processes
Jiahao Jiang
A central question in the prediction of stochastic processes is the extent to which past information can improve the probability of correctly predicting the next state. We introduce the Memory Prediction Excess (MPE) to address this question quantitatively. The MPE measures the average improvement in prediction accuracy obtained by using the entire observed history relative to using only the static marginal distribution, in discrete-time finite-state processes. It is defined as the difference between the expected optimal conditional prediction accuracy and the optimal static prediction accuracy. Its basic properties are examined: the MPE is always non-negative; it admits an upper bound depending on the static accuracy, attained if and only if the future is almost surely a deterministic function of the past; and degenerate cases in which the MPE vanishes are characterized. A normalized version, taking values in the unit interval, is introduced as a dimensionless measure of predictive efficiency. A lower bound is derived by comparing predictions based on histories of different lengths, showing that the expected optimal prediction accuracy is monotone with respect to the history…
PDF arXiv
arXiv cs.LGSep 28
Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting
Yuanyuan Deng, Mykola Pechenizkiy, Songgaojun Deng
Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference time, often leveraging delayed observed data to refine predictions. In the multivariate setting, distribution shifts often exhibit cross-variate dependencies, yet existing TSF-TTA methods adapt each variate independently and ignore this cross-variate structure. Exploiting such structure motivates cross-variate interaction, but coupling variates through backbone predictions introduces direct pathways for mixing uncorrected errors across variates, a concern under the delayed supervision of TSF-TTA. We identify the \emph{interaction space} as a key design choice, and show that acting on adapter corrections that refine backbone outputs, the \emph{correction space}, rather than on the predictions themselves, avoids directly propagating backbone errors across variates. We build on this to propose \textsc{CoRe} (\textsc{Co}rrection-space Interaction \textsc{Re}finement), realizing correction-space interaction through (i) Shared-anchor Correction Refinement (SCR), which combines each variate's correction with a shared anchor through a…
PDF arXiv
arXiv cs.LGSep 28
Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations
Oskar Bohn Lassen, Joao Paulo de Souza Boger, Simon Driscoll, Stephen I. Thomson, Sebastian Schemm +2
Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly, through tests of physical consistency. These metrics assess whether predictions are accurate and satisfy selected physical requirements, but provide limited insight into whether learned trajectories reproduce the underlying dynamics. Domain experts examine such relationships through physical representations that expose relevant processes, interactions, and responses, but these analyses are often separated from typical machine-learning evaluation. We introduce a practical framework for evaluating dynamical fidelity through predictive structure in physical representation spaces. Experts define the representations, while reference trajectories determine which relationships are predictive and retained as evaluation tests. We demonstrate the approach in atmospheric forecasting using ERA5 representations of planetary-wave activity and Northern Annular Mode evolution, and evaluate Pangu-Weather, GraphCast, and FengWu. The models exhibit distinct departures from reference predictive structure that are not reflected by conventional…
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 1h ago

Forecasting time

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

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