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Future Improvements

While the current deep learning approach provides strong predictive performance for store sales forecasting, there are several avenues for further improvement:

Advanced Model Architectures

  • Recurrent Neural Networks (RNNs):
    Implement RNNs, such as LSTM or GRU layers, to better capture sequential dependencies and long-term trends in time-series data.
  • Temporal Convolutional Networks (TCNs):
    Explore TCNs for their ability to model temporal relationships with convolutional layers.
  • Attention Mechanisms:
    Integrate attention layers to help the model focus on the most relevant time steps or features.

Hybrid and Ensemble Models

  • Hybrid Approaches:
    Combine deep learning models with tree-based methods (e.g., XGBoost, LightGBM) or statistical models (e.g., ARIMA, Prophet) to leverage the strengths of each.
  • Ensembling:
    Blend predictions from multiple models to reduce variance and improve robustness.

Feature Enrichment

  • External Data Sources:
    Incorporate additional data such as weather, economic indicators, or local events to provide more context for sales fluctuations.
  • Automated Feature Selection:
    Use feature selection techniques or embedding layers to identify and utilize the most informative features.

Training and Evaluation Enhancements

  • Hyperparameter Optimization:
    Apply automated tools like Optuna or Keras Tuner for more thorough hyperparameter search.
  • Cross-Validation:
    Implement time-series cross-validation (e.g., rolling or expanding window) to better estimate model generalization and avoid overfitting.

Model Interpretability

  • Explainability Tools:
    Use SHAP, LIME, or similar tools to interpret model predictions and understand feature importance, aiding business decision-making.

Model Selection for Time-Series

While deep learning models are powerful and flexible, they can be overkill for many time-series forecasting problems, especially when data is limited or patterns are well-captured by simpler models. Classical approaches such as ARIMA, SARIMA, Exponential Smoothing, or tree-based models like XGBoost often provide competitive or superior results with less computational overhead and easier interpretability. For many business applications, these models may be preferable unless the dataset is large, highly complex, or contains significant nonlinearities that deep learning can uniquely exploit.


Continued experimentation and innovation in these areas can further improve forecasting accuracy and business value.