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Open problems in Forecasting time

Event-driven forecasting with noisy and sparse signals1 paper raises it · 2 address it
Real-world time series are often influenced by exogenous events, but current methods struggle to retrieve, filter, and reason about these events causally.
- SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecastingraises · addresses
- TimeBraid: Unifying Time Series and Language for Understanding and Forecastingaddresses
Dependence structure in multivariate trajectory forecasting1 paper raises it · 1 addresses it
Most models predict marginal quantiles, ignoring the joint dependence of future paths, which is critical for downstream tasks like cumulative demand or peak load.
- PathFM: Fast Generative Forecasting of Multivariate Trajectoriesraises · addresses
Intervention-aware forecasting under covariate shifts1 paper raises it · 1 addresses it
Models must reliably forecast responses to future events that set covariates to unseen values, but current benchmarks and methods fall short.
- WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecastingraises · addresses
Evaluation bias from wet-biased data pipelines1 paper raises it · 1 addresses it
Generative models for precipitation nowcasting are misled by target-dependent sampling, which distorts calibration assessment and model development.
- Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcastingraises · addresses
Individual-level fidelity in agentic forecasting1 paper raises it · 0 address it
LLMs used as user simulators often fail to reproduce fine-grained decision logic and portfolio trajectories, despite coarse behavioral agreement.
- Beyond Behavioral Accuracy: Evaluating Individual-Level Fidelity in LLM Investor Simulationraises

Named by an AI from these papers' abstracts.

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Topics in this field

forecasting time · forecasters · forecasting189 papers28 this week
weather forecasting · weather prediction · forecasts155 papers23 this week
financial forecasting · based trading · price forecasting72 papers17 this week
data assimilation · forecast · probabilistic forecasting61 papers7 this week

Topics in this field

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arXiv cs.LGOct 18
Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting
Jingru Fei, Wei Fan, Kun Yi, Qi Zhang, Zhendong Niu
Explicitly modeling periodicity has proven to be an efficient approach for time series forecasting, where the residual, obtained after removing the periodic component from the time series, is typically treated as unstructured noise. However, in this work, we show that such residuals still exhibit structured, \textit{phase-dependent} deviations, as supported by both empirical observations and theoretical analysis. This suggests that residuals should not be treated merely as unstructured noise, but instead as \textit{phase-conditioned signals} whose variations arise in both direction and magnitude. Motivated by this perspective and inspired by phase and amplitude modulation in classical signal processing, we propose \textit{Paloma}, a simple yet effective framework for modeling residual deviations. Specifically, Paloma comprises two complementary modules: \textit{a Phase Rotation module}, which performs phase-conditioned rotations to capture directional changes in the feature space, and \textit{an Amplitude Scaling module}, which applies phase-conditioned affine transformations to model magnitude variations. We further abstract the two modules into a general-purpose plugin, termed…
PDF arXiv
arXiv cs.LGOct 18
The Hidden Look-Ahead: Point-in-Time Leakage in Electricity Market Data
Abdelkrim Alfalah
Empirical studies of electricity prices routinely state that their models use only information available at the time of the trading decision. For weather-driven forecasts drawn from commercial archives, this condition is harder to meet than it appears, and whether it holds can change over time. We identify three timing pitfalls. A forecast run is labelled by the issue time of its weather model, but becomes available hours later and keeps changing after publication. Auction gates follow local clock time, while archives use UTC. Outturn series are revised after publication without earlier values being kept. Using the timestamps of 128,656 forecast runs from ten weather-model configurations, matched to the day-ahead and first intraday auction gates in Great Britain over 2022-2026, we find that selecting "the latest run issued before the gate" picks a run not yet published at the gate on more than 99% of delivery days for six of the ten configurations at one of the two gates. For GFS at the day-ahead gate, the share is 0.6% in winter and 99.8% in summer. The exposure also shifts as publication delays drift, and an action margin of a few minutes can cost a full six-hour forecast…
PDF arXiv
arXiv cs.LGOct 18
Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks
James Kean, Aidan O'Sullivan
Accurate day-ahead wind-power production forecasting is increasingly important for energy system operations, market activity and balancing Great Britain's grid as wind generation grows. In this study, we train multi-site deep neural networks (DNNs) using pooled historical production and reanalysis weather data from 88 onshore wind Balancing Mechanism Units (BMUs). Forecasts are produced at 09:00 UK time for the following midnight-to-midnight delivery day, matching NESO's operational day-ahead forecasting schedule. We use reanalysis weather as future covariates during training and numerical weather prediction (NWP) forecasts during inference, extending training beyond the available historical NWP archive. Our multi-site recurrent neural network outperforms site-specific RNNs and a multi-site XGBoost model, while processing the full future-weather trajectory for the forecast horizon further improves accuracy relative to hour-aligned approaches. Our best model, an RNN with a bidirectional weather encoder (BiRNN) for NWP trajectory processing, reduces MW mean absolute error and root mean squared error relative to NESO by 17.94% and 12.51%, respectively, across 638,246 matched…
PDF arXiv
arXiv cs.LGOct 18
Data-driven forecasting of tropical cloud forest phenology: A dynamical systems and machine learning hybrid approach
Pedro Garcia, Saul Flores
Forecasting how species and ecosystems respond to climate change is a critical challenge in ecology, particularly in hyper-diverse tropical biomes. While traditional phenological models often rely on preconceived functional forms, this study introduces a data-driven hybrid approach that integrates dynamical systems theory with machine learning to forecast flowering and fruiting cycles. Using a robust 35-year dataset from a Venezuelan tropical cloud forest, we reconstruct the system's state-space using delay vectors inspired by Takens' theorem, incorporating climate forcing variables such as temperature and precipitation. The model was trained using Gaussian Process Regression, providing both point predictors and explicit uncertainty estimates. Our results clearly suggest that the synergy between dynamical systems and machine learning effectively captures the non-linear complexities of ecological time series. Specifically, the model achieved higher predictive accuracy for flowering and fruiting. This hybrid framework offers a scalable tool for anticipating phenological mismatches and informing conservation strategies in the face of global environmental change.
PDF arXiv
arXiv cs.AIOct 18
BrandEval: A Two-Track Multi-Agent Benchmark for Risk-Sensitive LLM Crisis Communication
Haotian Gan, Weidong Tang, Hanbin Sun, Yi Luo, Xintian Li +3
Evaluating LLMs for crisis communication requires more than judging isolated responses, because public feedback and competitor actions can reshape trust over time. Existing benchmarks remain largely static and single-turn, offering limited evidence about long-horizon risk-sensitive communication. We introduce \textbf{BrandEval}, a two-track benchmark for product-crisis communication, focusing on public brand responses to product-related safety, quality, service, and trust incidents. BrandEval-Static evaluates stakeholder-aware strategic analysis, while BrandEval-Dynamic stress-tests response policies in \textbf{BrandPolis}, a controlled multi-agent sandbox with consumer feedback, competitor responses, and trust-risk trajectories. We also construct \textbf{BrandSRD}, a Chinese crisis-response dataset, and instantiate a reference Strategic Agent using \textbf{Strategic Rationale (SR)}. Across proprietary and open-source models, we find that static strategic quality is informative but incomplete: it aligns with some end-state outcomes but does not reliably predict volatility and delayed recovery; dynamic stress tests further expose tail-risk failures under manipulative or weakly…
PDF arXiv
arXiv cs.LGOct 18
World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets
Karthik Iyer, Madan Dabbeeru
A retailer makes different decisions at different grains of the product, store and time hierarchy: store orders for an item in a store, supplier funding for an item across stores, category performance by store, and the financial plan for the chain. Which forecasting model to use depends on the grain. This paper compares models at each grain on four public data sets (M5, six Dominick's categories, Breakfast at the Frat, and Corporación Favorita, the last kept out of all development), over rolling forecast origins with paired tests and model confidence sets. It covers price and promotion through the plan inputs these data record, not product, assortment or place. The models are a world model that carries each item's state forward under the price and promotion plan, the same network without that state, LightGBM, the Temporal Fusion Transformer, TimesFM, Chronos-Bolt, Timestral, seasonal naive, and an equal-weight average of the world model, LightGBM and TimesFM given the plan. The answer changed with the grain. For an item by store and an item across stores, the average had the lowest error on seven and eight of nine data sets, and the 90% confidence set held a median of 1 and 2…
PDF arXiv

Papers

Topics in this field

forecasting time · forecasters · forecasting189 papers28 this week
weather forecasting · weather prediction · forecasts155 papers23 this week
financial forecasting · based trading · price forecasting72 papers17 this week
data assimilation · forecast · probabilistic forecasting61 papers7 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Open problems in Forecasting time

Event-driven forecasting with noisy and sparse signals1 paper raises it · 2 address it
Real-world time series are often influenced by exogenous events, but current methods struggle to retrieve, filter, and reason about these events causally.
- SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecastingraises · addresses
- TimeBraid: Unifying Time Series and Language for Understanding and Forecastingaddresses
Dependence structure in multivariate trajectory forecasting1 paper raises it · 1 addresses it
Most models predict marginal quantiles, ignoring the joint dependence of future paths, which is critical for downstream tasks like cumulative demand or peak load.
- PathFM: Fast Generative Forecasting of Multivariate Trajectoriesraises · addresses
Intervention-aware forecasting under covariate shifts1 paper raises it · 1 addresses it
Models must reliably forecast responses to future events that set covariates to unseen values, but current benchmarks and methods fall short.
- WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecastingraises · addresses
Evaluation bias from wet-biased data pipelines1 paper raises it · 1 addresses it
Generative models for precipitation nowcasting are misled by target-dependent sampling, which distorts calibration assessment and model development.
- Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcastingraises · addresses
Individual-level fidelity in agentic forecasting1 paper raises it · 0 address it
LLMs used as user simulators often fail to reproduce fine-grained decision logic and portfolio trajectories, despite coarse behavioral agreement.
- Beyond Behavioral Accuracy: Evaluating Individual-Level Fidelity in LLM Investor Simulationraises

Named by an AI from these papers' abstracts.

Fields · AI · updated 52m ago

Forecasting time

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

75papers this week−17.6% vs last week

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Recent papers

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arXiv cs.LGOct 18
Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting
Jingru Fei, Wei Fan, Kun Yi, Qi Zhang, Zhendong Niu
Explicitly modeling periodicity has proven to be an efficient approach for time series forecasting, where the residual, obtained after removing the periodic component from the time series, is typically treated as unstructured noise. However, in this work, we show that such residuals still exhibit structured, \textit{phase-dependent} deviations, as supported by both empirical observations and theoretical analysis. This suggests that residuals should not be treated merely as unstructured noise, but instead as \textit{phase-conditioned signals} whose variations arise in both direction and magnitude. Motivated by this perspective and inspired by phase and amplitude modulation in classical signal processing, we propose \textit{Paloma}, a simple yet effective framework for modeling residual deviations. Specifically, Paloma comprises two complementary modules: \textit{a Phase Rotation module}, which performs phase-conditioned rotations to capture directional changes in the feature space, and \textit{an Amplitude Scaling module}, which applies phase-conditioned affine transformations to model magnitude variations. We further abstract the two modules into a general-purpose plugin, termed…
PDF arXiv
arXiv cs.LGOct 18
The Hidden Look-Ahead: Point-in-Time Leakage in Electricity Market Data
Abdelkrim Alfalah
Empirical studies of electricity prices routinely state that their models use only information available at the time of the trading decision. For weather-driven forecasts drawn from commercial archives, this condition is harder to meet than it appears, and whether it holds can change over time. We identify three timing pitfalls. A forecast run is labelled by the issue time of its weather model, but becomes available hours later and keeps changing after publication. Auction gates follow local clock time, while archives use UTC. Outturn series are revised after publication without earlier values being kept. Using the timestamps of 128,656 forecast runs from ten weather-model configurations, matched to the day-ahead and first intraday auction gates in Great Britain over 2022-2026, we find that selecting "the latest run issued before the gate" picks a run not yet published at the gate on more than 99% of delivery days for six of the ten configurations at one of the two gates. For GFS at the day-ahead gate, the share is 0.6% in winter and 99.8% in summer. The exposure also shifts as publication delays drift, and an action margin of a few minutes can cost a full six-hour forecast…
PDF arXiv
arXiv cs.LGOct 18
Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks
James Kean, Aidan O'Sullivan
Accurate day-ahead wind-power production forecasting is increasingly important for energy system operations, market activity and balancing Great Britain's grid as wind generation grows. In this study, we train multi-site deep neural networks (DNNs) using pooled historical production and reanalysis weather data from 88 onshore wind Balancing Mechanism Units (BMUs). Forecasts are produced at 09:00 UK time for the following midnight-to-midnight delivery day, matching NESO's operational day-ahead forecasting schedule. We use reanalysis weather as future covariates during training and numerical weather prediction (NWP) forecasts during inference, extending training beyond the available historical NWP archive. Our multi-site recurrent neural network outperforms site-specific RNNs and a multi-site XGBoost model, while processing the full future-weather trajectory for the forecast horizon further improves accuracy relative to hour-aligned approaches. Our best model, an RNN with a bidirectional weather encoder (BiRNN) for NWP trajectory processing, reduces MW mean absolute error and root mean squared error relative to NESO by 17.94% and 12.51%, respectively, across 638,246 matched…
PDF arXiv
arXiv cs.LGOct 18
Data-driven forecasting of tropical cloud forest phenology: A dynamical systems and machine learning hybrid approach
Pedro Garcia, Saul Flores
Forecasting how species and ecosystems respond to climate change is a critical challenge in ecology, particularly in hyper-diverse tropical biomes. While traditional phenological models often rely on preconceived functional forms, this study introduces a data-driven hybrid approach that integrates dynamical systems theory with machine learning to forecast flowering and fruiting cycles. Using a robust 35-year dataset from a Venezuelan tropical cloud forest, we reconstruct the system's state-space using delay vectors inspired by Takens' theorem, incorporating climate forcing variables such as temperature and precipitation. The model was trained using Gaussian Process Regression, providing both point predictors and explicit uncertainty estimates. Our results clearly suggest that the synergy between dynamical systems and machine learning effectively captures the non-linear complexities of ecological time series. Specifically, the model achieved higher predictive accuracy for flowering and fruiting. This hybrid framework offers a scalable tool for anticipating phenological mismatches and informing conservation strategies in the face of global environmental change.
PDF arXiv
arXiv cs.AIOct 18
BrandEval: A Two-Track Multi-Agent Benchmark for Risk-Sensitive LLM Crisis Communication
Haotian Gan, Weidong Tang, Hanbin Sun, Yi Luo, Xintian Li +3
Evaluating LLMs for crisis communication requires more than judging isolated responses, because public feedback and competitor actions can reshape trust over time. Existing benchmarks remain largely static and single-turn, offering limited evidence about long-horizon risk-sensitive communication. We introduce \textbf{BrandEval}, a two-track benchmark for product-crisis communication, focusing on public brand responses to product-related safety, quality, service, and trust incidents. BrandEval-Static evaluates stakeholder-aware strategic analysis, while BrandEval-Dynamic stress-tests response policies in \textbf{BrandPolis}, a controlled multi-agent sandbox with consumer feedback, competitor responses, and trust-risk trajectories. We also construct \textbf{BrandSRD}, a Chinese crisis-response dataset, and instantiate a reference Strategic Agent using \textbf{Strategic Rationale (SR)}. Across proprietary and open-source models, we find that static strategic quality is informative but incomplete: it aligns with some end-state outcomes but does not reliably predict volatility and delayed recovery; dynamic stress tests further expose tail-risk failures under manipulative or weakly…
PDF arXiv
arXiv cs.LGOct 18
World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets
Karthik Iyer, Madan Dabbeeru
A retailer makes different decisions at different grains of the product, store and time hierarchy: store orders for an item in a store, supplier funding for an item across stores, category performance by store, and the financial plan for the chain. Which forecasting model to use depends on the grain. This paper compares models at each grain on four public data sets (M5, six Dominick's categories, Breakfast at the Frat, and Corporación Favorita, the last kept out of all development), over rolling forecast origins with paired tests and model confidence sets. It covers price and promotion through the plan inputs these data record, not product, assortment or place. The models are a world model that carries each item's state forward under the price and promotion plan, the same network without that state, LightGBM, the Temporal Fusion Transformer, TimesFM, Chronos-Bolt, Timestral, seasonal naive, and an equal-weight average of the world model, LightGBM and TimesFM given the plan. The answer changed with the grain. For an item by store and an item across stores, the average had the lowest error on seven and eight of nine data sets, and the 90% confidence set held a median of 1 and 2…
PDF arXiv