N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting (2201.12886v6)

Abstract: Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two common challenges afflicting the task are the volatility of the predictions and their computational complexity. We introduce N-HiTS, a model which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. These techniques enable the proposed method to assemble its predictions sequentially, emphasizing components with different frequencies and scales while decomposing the input signal and synthesizing the forecast. We prove that the hierarchical interpolation technique can efficiently approximate arbitrarily long horizons in the presence of smoothness. Additionally, we conduct extensive large-scale dataset experiments from the long-horizon forecasting literature, demonstrating the advantages of our method over the state-of-the-art methods, where N-HiTS provides an average accuracy improvement of almost 20% over the latest Transformer architectures while reducing the computation time by an order of magnitude (50 times). Our code is available at bit.ly/3VA5DoT

• The paper introduces N-HiTS, a neural model using hierarchical interpolation and multi-rate sampling to enhance long-horizon forecasting accuracy.
• The paper reduces computational complexity by integrating sub-sampling layers that effectively manage long-range dependencies.
• The paper demonstrates a 20% accuracy gain and a 50-fold decrease in computation time across multiple large-scale datasets.

An Expert Review of "N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting"

The paper "N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting" presents an innovative methodology designed to enhance the capabilities of neural networks in handling long-horizon forecasting tasks. This task has historically been fraught with challenges such as volatility in predictions and significant computational complexity. The authors introduce the N-HiTS model, which leverages hierarchical interpolation and multi-rate data sampling techniques to address these issues. The design of N-HiTS is particularly noteworthy for its focus on maintaining computational efficiency while improving forecast accuracy, as demonstrated through extensive empirical evaluations.

Key Methodological Contributions

The primary contributions of N-HiTS revolve around two novel components: multi-rate data sampling and hierarchical interpolation. These components enable the model to decompose time series data effectively and tailor predictions to different frequencies and scales.

1. Multi-Rate Data Sampling: This approach applies sub-sampling layers before the fully-connected layers in the model. This subsampling significantly reduces the memory and computational burden, allowing the model to manage long-range dependencies effectively.
2. Hierarchical Interpolation: The model further reduces forecast volatility through hierarchical interpolation, which aligns the neural network's prediction scale with the desired output resolution. This method involves a multi-scale approach that builds upon previous layers' simplifications of input data, greatly enhancing the model's capacity to generalize long-horizon forecasts.

Experimental Evaluation and Results

The evaluation of N-HiTS was conducted using a suite of six large-scale datasets commonly used in long-horizon forecasting research, including datasets on electricity consumption and weather patterns. The approach demonstrated an average accuracy improvement of nearly 20% over recent Transformer architectures for similar tasks while achieving a 50-fold reduction in computation time. These results are substantial, indicating that the N-HiTS model not only enhances predictive accuracy but does so with significantly greater computational efficiency.

Implications and Future Directions

The implications of N-HiTS are profound, both from theoretical and practical perspectives. Theoretically, the introduction of hierarchical interpolation within a neural network context offers a fresh understanding of how multi-scale time-series data can be optimally processed. Practically, the reduced computation time and resource requirements enhance the feasibility of deploying such models in real-world scenarios, particularly in situations where computational resources may be limited.

The research opens several avenues for future work. One potential direction could involve integrating N-HiTS methodologies with existing Transformer-based architectures to capitalize on their respective strengths. Additionally, exploring the theoretical underpinnings of hierarchical interpolation could yield further insights into optimizing neural network architectures for various time-series forecasting tasks.

Conclusion

In conclusion, the N-HiTS model represents a significant advancement in the field of time-series forecasting, addressing critical limitations in existing methodologies. Its dual emphasis on accuracy and computational efficiency positions it as a valuable tool in the evolving landscape of neural forecasting technologies. This paper provides a strong foundation for further research and development in neural network-based forecasting, offering clear evidence of its potential benefits in both academic and applied settings.