AUNET: Attention-Based Time Series Forecasting
AUNET is a neural network architecture designed for time series forecasting, combining multi-head self-attention with dense layers to capture temporal patterns in numeric datasets. Developed using TensorFlow/Keras, it supports customizable input windows and forecast horizons.
Usage
from aunet_model import AUNET
model = AUNET(input_length=30, forecast_horizon=7)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
Model Details
- Architecture: Multi-head self-attention + dense layers
- Framework: TensorFlow / Keras
- Use Case: Multistep forecasting of univariate or multivariate numeric time series
- Target Feature: Second column of data (post date-drop)
Authors
- Adria Binte Habib
- Dr. Golam Rabiul Alam
- Dr. Zia Uddin
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