Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers

• URL: http://arxiv.org/abs/2601.13144v1
• Date: Mon, 19 Jan 2026 15:25:04 GMT
• Title: Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers
• Authors: Jonas Tirona, Sarang Patil, Spiridon Kasapis, Eren Dogan, John Stefan, Irina N. Kitiashvili, Alexander G. Kosovichev, Mengjia Xu,
• Abstract summary: Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting.<n>We investigate a sliding-window Transformer architecture to forecast continuum intensity evolution up to 12 hours ahead using data from 46 ARs observed by SDO/HMI.<n>Our best-performing model, combining the Early Detection architecture without the Conv1D layer, achieved a Root Mean Square Error (RMSE) of 0.1189.<n>While the Transformer demonstrates superior aggregate timing and accuracy, we note that this high-sensitivity detection comes with increased variance compared to smoother baseline models.
• Score: 34.25104679311873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting. Building on established Long Short-Term Memory (LSTM) based approaches for forecasting the continuum intensity decrease associated with AR emergence, this work expands the modeling with new architectures and targets. We investigate a sliding-window Transformer architecture to forecast continuum intensity evolution up to 12 hours ahead using data from 46 ARs observed by SDO/HMI. We conduct a systematic ablation study to evaluate two key components: (1) the inclusion of a temporal 1D convolutional (Conv1D) front-end and (2) a novel `Early Detection' architecture featuring attention biases and a timing-aware loss function. Our best-performing model, combining the Early Detection architecture without the Conv1D layer, achieved a Root Mean Square Error (RMSE) of 0.1189 (representing a 10.6% improvement over the LSTM baseline) and an average advance warning time of 4.73 hours (timing difference of -4.73h), even under a stricter emergence criterion than previous studies. While the Transformer demonstrates superior aggregate timing and accuracy, we note that this high-sensitivity detection comes with increased variance compared to smoother baseline models. However, this volatility is a necessary trade-off for operational warning systems: the model's ability to detect micro-changes in precursor signals enables significantly earlier detection, outweighing the cost of increased noise. Our results demonstrate that Transformer architectures modified with early detection biases, when used without temporal smoothing layers, provide a high-sensitivity alternative for forecasting AR emergence that prioritizes advance warning over statistical smoothness.

Related papers

• Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling [0.0]
  We introduce two proactive anomaly detection frameworks: the Forward Forecasting Model (FFM) and the Backward Reconstruction Model (BRM)<n>FFM forecasts future sequences to anticipate disruptions, while BRM reconstructs recent history from future context to uncover early precursors.<n>Our models support both continuous and discrete multivariate features, enabling robust performance in real-world settings.
  arXiv  Detail & Related papers  (2026-02-12T03:57:41Z)
• EVEREST: An Evidential, Tail-Aware Transformer for Rare-Event Time-Series Forecasting [4.551615447454767]
  EVEREST is a transformer-based architecture for probabilistic rare-event forecasting.<n>It delivers calibrated predictions and tail-aware risk estimation.<n>It is applicable to high-stakes domains such as industrial monitoring, weather, and satellite diagnostics.
  arXiv  Detail & Related papers  (2026-01-26T23:15:20Z)
• SARIMAX-Based Power Outage Prediction During Extreme Weather Events [9.853802100348892]
  This study develops a SARIMAX-based prediction system for short-term power outage forecasting during extreme weather events.<n>Using hourly data from Michigan counties with outage counts and comprehensive weather features, we implement a systematic two-stage feature engineering pipeline.<n>The selected features are augmented with temporal embeddings, multi-scale lag features, and weather variables with their corresponding lags as inputs to the SARIMAX model.
  arXiv  Detail & Related papers  (2025-11-02T17:13:58Z)
• SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization [62.958457694151384]
  We introduce preference optimization into precipitation nowcasting for the first time, motivated by the success of reinforcement learning from human feedback in large language models.<n>In the first stage, the framework focuses on reducing FAR, training the model to effectively suppress false alarms.
  arXiv  Detail & Related papers  (2025-10-22T16:11:22Z)
• ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections [0.0]
  ARCANE is the first framework explicitly designed for early ICME detection in streaming solar wind data.<n>We find that our detection pipeline achieves an F1-Score of 0.37, with an average detection delay of 24.5% of the event's duration.
  arXiv  Detail & Related papers  (2025-05-14T13:17:45Z)
• Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism [0.40964539027092917]
  Bearing fault detection is a critical task in predictive maintenance, where accurate and timely fault identification can prevent costly downtime and equipment damage.<n>Traditional attention mechanisms in Transformer neural networks often struggle to capture the complex temporal patterns in bearing vibration data, leading to suboptimal performance.<n>We propose a novel attention mechanism, Temporal Decomposition Attention (TDA), which combines temporal bias encoding with seasonal-trend decomposition to capture both long-term dependencies and periodic fluctuations in time series data.
  arXiv  Detail & Related papers  (2024-12-15T16:51:31Z)
• Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks [44.99833362998488]
  We develop Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Time-series datasets of acoustic power and magnetic flux are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease.
  arXiv  Detail & Related papers  (2024-09-25T23:09:46Z)
• A Novel Hybrid Approach for Tornado Prediction in the United States: Kalman-Convolutional BiLSTM with Multi-Head Attention [9.51657235413336]
  Tornadoes are among the most intense atmospheric vortex phenomena and pose significant challenges for detection and forecasting. Conventional methods, which heavily depend on ground-based observations and radar data, are limited by issues such as decreased accuracy over greater distances and a high rate of false positives. This study utilizes the Seamless Hybrid Scan Reflectivity dataset from the Multi-Radar Multi-Sensor (MRMS) system to enhance accuracy. A novel hybrid model, the Kalman-Convolutional BiLSTM with Multi-Head Attention, is introduced to improve dynamic state estimation and capture both spatial and temporal dependencies within the data.
  arXiv  Detail & Related papers  (2024-08-05T18:11:23Z)
• Multi-scale Spatio-temporal Transformer-based Imbalanced Longitudinal Learning for Glaucoma Forecasting from Irregular Time Series Images [45.894671834869975]
  Glaucoma is one of the major eye diseases that leads to progressive optic nerve fiber damage and irreversible blindness. We introduce the Multi-scale Spatio-temporal Transformer Network (MST-former) based on the transformer architecture tailored for sequential image inputs. Our method shows excellent generalization capability on the Alzheimer's Disease Neuroimaging Initiative (ADNI) MRI dataset, with an accuracy of 90.3% for mild cognitive impairment and Alzheimer's disease prediction.
  arXiv  Detail & Related papers  (2024-02-21T02:16:59Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.