This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Temporal Attention-based Long-Short Term Memory (TALSTM) Challenge
In this challenge, we'll push the limits of Long Short-Term Memory (LSTM) networks by introducing a novel temporal attention mechanism. Your task is to design, train, and evaluate a TALSTM model that can learn to forecast the trajectory of a dynamic system with multiple, interacting components.
Constraints:
-
Multi-modal input: Your model will accept three types of input data:
- Time-series data (e.g., sensor readings) with 10 features
- Image data (e.g., camera feeds) with 3 color channels
- Text data (e.g., system logs) with 5 keywords
-
Dynamic system: The system consists of 5 interacting components, each with its own temporal dynamics. The components are:
- Component A: Temperature (time-series)
- Component B: Humidity (time-series)
- Component C: Air pressure (time-series)
- Component D: Visual sensor (image)
- Component E: System alert logs (text)
- Forecasting horizon: Your model must predict the values of each component for the next 24 hours.
- Model evaluation: We'll use a combination of metrics, including mean absolute error (MAE), mean squared error (MSE), and precision@k for the top-k most accurate predictions.
- Temporal attention mechanism: Your model must incorporate a novel temporal attention mechanism that adapts to the varying temporal relationships between the interacting components.
- Model size: The total number of model parameters must not exceed 100 million.
Dataset:
We provide a pre-processed dataset consisting of 1 million samples, each with the three input modalities and 5 target components. The dataset is available on a private cloud storage service.
Evaluation metrics:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Precision@k (k=5)
Submission guidelines:
- Implement your TALSTM model using a deep learning framework (e.g., PyTorch, TensorFlow).
- Train your model on the provided dataset for a maximum of 100 epochs.
- Evaluate your model using the provided evaluation metrics.
- Submit your model implementation, along with the evaluation results, to a designated GitHub repository.
Deadline:
Submissions are due on December 15, 2025. Late submissions will not be accepted.
Prizes:
The top 3 submissions will receive prizes of $10,000, $5,000, and $2,000, respectively.
Are you up for the challenge?
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This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Dr. Carlos Ruiz Viquez | Sciencx (2025-11-26T20:31:49+00:00) **Temporal Attention-based Long-Short Term Memory (TALSTM) C. Retrieved from https://www.scien.cx/2025/11/26/temporal-attention-based-long-short-term-memory-talstm-c/
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