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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 · forecasting193 papers32 this week
weather forecasting · weather prediction · forecasts159 papers27 this week
financial forecasting · based trading · price forecasting74 papers19 this week
data assimilation · forecast · probabilistic forecasting63 papers9 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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arXiv cs.CLOct 17
Backend-Frontend Converter: Component-Based Conversion of .NET Computational Models to TypeScript
Petr Ivankov, Max Shestov
Computational models developed in .NET often need to run locally within web applications. We present Backend--Frontend Converter, an architecture for generating TypeScript implementations of component-based models. The converter maps components, connections, aliases, and formula trees to model-specific TypeScript code. Reusable runtime libraries provide numerical integration, data access, and state handling. We evaluate stored generated implementations through four orbital scenarios and a recorded trading-strategy comparison. Orbital calculations execute in a browser and are paired with C\# results by crossing event and timestamp; trading outputs are aligned by date. Analytic scheduler tests and deliberately perturbed outputs assess the validation procedure. Each orbital scenario produces 91 paired events with identical timestamps. Position and velocity differences remain below $(10^{-6}\)~km$ and $(10^{-9}\)~km/s$. All 20 analytic scheduler checks pass, and the comparator detects each of three prescribed state and time perturbations. The trading comparison contains 501 aligned daily records with matching indicator values, positions, and order events. The evaluated…
PDF arXiv
arXiv cs.CEOct 17
Learning volatility-adaptive forgetting via GARCH-modulated LSTM for stock price forecasting
Xin-Rui Lu, Da-Qing Zhang
Stock price forecasting is hindered by the heteroscedastic nature of financial time series, in which turbulent and tranquil regimes alternate. Although the Long Short-Term Memory (LSTM) network is widely used for this task, its gates are oblivious to such volatility variation, so memory retention remains fixed regardless of market conditions. A common remedy is to supply volatility estimates as an additional input feature, yet this leaves the internal memory dynamics untouched. Instead, volatility can serve as a direct regulatory signal over the gate activation itself. This paper presents the Gsigmoid-LSTM model, which embeds Generalized Autoregressive Conditional Heteroscedasticity (GARCH(1,1))-derived volatility estimates into the LSTM forgetting gate through the Gsigmoid activation function, a volatility-adaptive variant of the sigmoid that combines a volatility adjustment factor with slope regulation via a learnable parameter. The model thereby automatically prioritizes recent information during turbulent periods while preserving long-term dependencies in stable markets. In the engineering application of daily stock price forecasting, extensive experiments, including…
PDF arXiv
arXiv cs.CEOct 17
A decomposition-integration framework coupling singular spectrum analysis with a volatility-adaptive LSTM for stock price forecasting
Xin-Rui Lu, Da-Qing Zhang
Financial time series exhibit multi-scale, high-noise, and non-stationary volatility dynamics. Decomposition--integration frameworks built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) have advanced this field, yet existing CEEMDAN hybrids remain unstable: high-frequency mode mixing contaminates downstream predictors, and the heterogeneous volatility of decomposed components is not matched by any volatility-adaptive mechanism. To address these gaps, this paper proposes CEEMDAN-SE-SSA-VMGL. CEEMDAN adaptively decomposes the original series, and sample-entropy-driven K-means clustering groups the intrinsic mode functions into high-, mid-, and low-frequency and residual components. Singular spectrum analysis (SSA) is then applied to the high-frequency group, whose singular-value decomposition of the trajectory matrix and principal-component reconstruction decouple intertwined pseudo-oscillatory modes and alleviate mode mixing. We further design a plug-and-play robust LSTM variant, the volatility-modulated GARCH-LSTM (VMGL), which embeds a differentiable GARCH(1,1) layer inside the LSTM cell as a risk gate; the estimated conditional volatility…
PDF arXiv
arXiv cs.LGOct 16
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
Aakriti, Manoj Cherukumalli, Prathamesh Patil, Leonidas Tsepenekas, Amit Varshney +3
A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ. The number of targets and covariates changes from dataset to dataset, horizons vary by orders of magnitude, and sampling rates range from seconds to years. Existing time-series foundation models address these only in part, since they isolate each series or visit the two grid relations in separate blocks, and they fix the horizon and resolution before training begins. We introduce Morpheus-2.0, a compact encoder-only foundation model that represents targets and covariates on a single variable-time grid. Its dual-axis attention runs both relations side by side in every layer, one across variables at a timestep and one across time within a variable; a learned gate weights the two per layer, and an axis-aware rotary encoding preserves both grid coordinates. The forecast length is sampled during training, so one set of weights spans short to long horizons at no additional inference cost. Morpheus-2.0 Multiscale extends the model to several temporal resolutions at once, weighting them according to the series being forecast. Across…
PDF arXiv
arXiv cs.AIOct 16
From SEC Filings to Financial Signals: Evaluating LLMs on Stock Market Reactions and Analyst Forecasts
Shengkun Wang, Taoran Ji, Linhan Wang, Sattar Mansi, Chang-Tien Lu +1
Form 10-Q MD&A sections combine long-form management narrative with accounting and forward-looking information. Benchmarks for financial filings commonly evaluate question answering or numerical reasoning, leaving unclear whether outputs from large language models (LLMs) align with market and analyst outcomes. We evaluate prompted, zero-shot outputs from 13 general-purpose and finance-specialized LLMs on 45,964 Form 10-Q filing events from 2001 to 2024, linking MD&A text to cross-sectional fundamental ranks, pre-filing analyst forecasts, and realized abnormal returns. The evaluation covers return ranking under five controlled information views and next-quarter EPS forecasting under direct and consensus-anchored interfaces. Relative to disclosure alone, adding cross-sectional fundamental ranks raises mean effective-date rank correlation (RankIC) by 0.0089 for effective-event-date returns and 0.0098 for following-day returns, whereas analyst-summary context provides no comparable average gain. Across the four disclosure-containing views, finance-specialized models show an effective-event-date group advantage but no statistically reliable following-day advantage, while…
PDF arXiv
arXiv physics.ao-phOct 16
Interpretability of AI weather models via intermediate decoding
Matthias Beylich, Kirsten I. Tempest, Tobias Selz, George C. Craig
AI weather prediction models are often regarded as black boxes. Despite their impressive skill, there is little understanding of how the input data is processed during inference to produce the forecast. This limitation arises from the use of high-dimensional latent representations in which individual dimensions are no longer directly interpretable in physical terms. To gain insight into how forecasts are constructed, we propose the method of intermediate decoding for AI weather models and apply it to GraphCast, Aurora and ArchesWeather. In this approach, latent states from intermediate depths are decoded back into meteorological fields. To do this, we use the decoder that has been trained to output the forecast at the end of processing. Since latent states at different depths do not share a consistent basis, we train affine transformations that align intermediate latent states with the decoder's expected input space. Applying this framework to the three models yields the following results. First, the skill of forecasts at intermediate stages of processing improves gradually through the processor layers. Second, bypassing all processor blocks shows that the encoder-decoder pair is…
PDF arXiv

Papers

Topics in this field

forecasting time · forecasters · forecasting193 papers32 this week
weather forecasting · weather prediction · forecasts159 papers27 this week
financial forecasting · based trading · price forecasting74 papers19 this week
data assimilation · forecast · probabilistic forecasting63 papers9 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.

Recent papers

All in Research →

arXiv cs.CLOct 17
Backend-Frontend Converter: Component-Based Conversion of .NET Computational Models to TypeScript
Petr Ivankov, Max Shestov
Computational models developed in .NET often need to run locally within web applications. We present Backend--Frontend Converter, an architecture for generating TypeScript implementations of component-based models. The converter maps components, connections, aliases, and formula trees to model-specific TypeScript code. Reusable runtime libraries provide numerical integration, data access, and state handling. We evaluate stored generated implementations through four orbital scenarios and a recorded trading-strategy comparison. Orbital calculations execute in a browser and are paired with C\# results by crossing event and timestamp; trading outputs are aligned by date. Analytic scheduler tests and deliberately perturbed outputs assess the validation procedure. Each orbital scenario produces 91 paired events with identical timestamps. Position and velocity differences remain below $(10^{-6}\)~km$ and $(10^{-9}\)~km/s$. All 20 analytic scheduler checks pass, and the comparator detects each of three prescribed state and time perturbations. The trading comparison contains 501 aligned daily records with matching indicator values, positions, and order events. The evaluated…
PDF arXiv
arXiv cs.CEOct 17
Learning volatility-adaptive forgetting via GARCH-modulated LSTM for stock price forecasting
Xin-Rui Lu, Da-Qing Zhang
Stock price forecasting is hindered by the heteroscedastic nature of financial time series, in which turbulent and tranquil regimes alternate. Although the Long Short-Term Memory (LSTM) network is widely used for this task, its gates are oblivious to such volatility variation, so memory retention remains fixed regardless of market conditions. A common remedy is to supply volatility estimates as an additional input feature, yet this leaves the internal memory dynamics untouched. Instead, volatility can serve as a direct regulatory signal over the gate activation itself. This paper presents the Gsigmoid-LSTM model, which embeds Generalized Autoregressive Conditional Heteroscedasticity (GARCH(1,1))-derived volatility estimates into the LSTM forgetting gate through the Gsigmoid activation function, a volatility-adaptive variant of the sigmoid that combines a volatility adjustment factor with slope regulation via a learnable parameter. The model thereby automatically prioritizes recent information during turbulent periods while preserving long-term dependencies in stable markets. In the engineering application of daily stock price forecasting, extensive experiments, including…
PDF arXiv
arXiv cs.CEOct 17
A decomposition-integration framework coupling singular spectrum analysis with a volatility-adaptive LSTM for stock price forecasting
Xin-Rui Lu, Da-Qing Zhang
Financial time series exhibit multi-scale, high-noise, and non-stationary volatility dynamics. Decomposition--integration frameworks built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) have advanced this field, yet existing CEEMDAN hybrids remain unstable: high-frequency mode mixing contaminates downstream predictors, and the heterogeneous volatility of decomposed components is not matched by any volatility-adaptive mechanism. To address these gaps, this paper proposes CEEMDAN-SE-SSA-VMGL. CEEMDAN adaptively decomposes the original series, and sample-entropy-driven K-means clustering groups the intrinsic mode functions into high-, mid-, and low-frequency and residual components. Singular spectrum analysis (SSA) is then applied to the high-frequency group, whose singular-value decomposition of the trajectory matrix and principal-component reconstruction decouple intertwined pseudo-oscillatory modes and alleviate mode mixing. We further design a plug-and-play robust LSTM variant, the volatility-modulated GARCH-LSTM (VMGL), which embeds a differentiable GARCH(1,1) layer inside the LSTM cell as a risk gate; the estimated conditional volatility…
PDF arXiv
arXiv cs.LGOct 16
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
Aakriti, Manoj Cherukumalli, Prathamesh Patil, Leonidas Tsepenekas, Amit Varshney +3
A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ. The number of targets and covariates changes from dataset to dataset, horizons vary by orders of magnitude, and sampling rates range from seconds to years. Existing time-series foundation models address these only in part, since they isolate each series or visit the two grid relations in separate blocks, and they fix the horizon and resolution before training begins. We introduce Morpheus-2.0, a compact encoder-only foundation model that represents targets and covariates on a single variable-time grid. Its dual-axis attention runs both relations side by side in every layer, one across variables at a timestep and one across time within a variable; a learned gate weights the two per layer, and an axis-aware rotary encoding preserves both grid coordinates. The forecast length is sampled during training, so one set of weights spans short to long horizons at no additional inference cost. Morpheus-2.0 Multiscale extends the model to several temporal resolutions at once, weighting them according to the series being forecast. Across…
PDF arXiv
arXiv cs.AIOct 16
From SEC Filings to Financial Signals: Evaluating LLMs on Stock Market Reactions and Analyst Forecasts
Shengkun Wang, Taoran Ji, Linhan Wang, Sattar Mansi, Chang-Tien Lu +1
Form 10-Q MD&A sections combine long-form management narrative with accounting and forward-looking information. Benchmarks for financial filings commonly evaluate question answering or numerical reasoning, leaving unclear whether outputs from large language models (LLMs) align with market and analyst outcomes. We evaluate prompted, zero-shot outputs from 13 general-purpose and finance-specialized LLMs on 45,964 Form 10-Q filing events from 2001 to 2024, linking MD&A text to cross-sectional fundamental ranks, pre-filing analyst forecasts, and realized abnormal returns. The evaluation covers return ranking under five controlled information views and next-quarter EPS forecasting under direct and consensus-anchored interfaces. Relative to disclosure alone, adding cross-sectional fundamental ranks raises mean effective-date rank correlation (RankIC) by 0.0089 for effective-event-date returns and 0.0098 for following-day returns, whereas analyst-summary context provides no comparable average gain. Across the four disclosure-containing views, finance-specialized models show an effective-event-date group advantage but no statistically reliable following-day advantage, while…
PDF arXiv
arXiv physics.ao-phOct 16
Interpretability of AI weather models via intermediate decoding
Matthias Beylich, Kirsten I. Tempest, Tobias Selz, George C. Craig
AI weather prediction models are often regarded as black boxes. Despite their impressive skill, there is little understanding of how the input data is processed during inference to produce the forecast. This limitation arises from the use of high-dimensional latent representations in which individual dimensions are no longer directly interpretable in physical terms. To gain insight into how forecasts are constructed, we propose the method of intermediate decoding for AI weather models and apply it to GraphCast, Aurora and ArchesWeather. In this approach, latent states from intermediate depths are decoded back into meteorological fields. To do this, we use the decoder that has been trained to output the forecast at the end of processing. Since latent states at different depths do not share a consistent basis, we train affine transformations that align intermediate latent states with the decoder's expected input space. Applying this framework to the three models yields the following results. First, the skill of forecasts at intermediate stages of processing improves gradually through the processor layers. Second, bypassing all processor blocks shows that the encoder-decoder pair is…
PDF arXiv

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