N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (1905.10437v4)

Abstract: We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

• The paper demonstrates that a deep neural architecture devoid of TS-specific components can outperform traditional statistical models by up to 11%.
• It introduces interpretable forecasts by decomposing predictions into trend and seasonal components using polynomial and Fourier bases.
• The N-BEATS model employs backward and forward residual links, enabling rapid training and cumulative refinement of time series forecasts.

An Insightful Analysis of N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting

The paper "N-BEATS: Neural basis expansion analysis for interpretable time series forecasting" introduces a novel deep learning architecture specifically designed for univariate time series (TS) forecasting. Developed by Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio, the model, known as Neural basis expansion analysis with interpretable outputs (N-BEATS), is characterized by backward and forward residual links and a deep stack of fully-connected layers. This architecture is both versatile across diverse TS domains and fast to train, making it a significant addition to the field of machine learning-driven forecasting.

Key Contributions

Deep Neural Architecture and State-of-the-Art Performance

The first notable contribution of this work is the demonstration that a deep learning model devoid of TS-specific components can outperform traditional statistical approaches on well-known datasets, such as M3, M4, and the tourism datasets. The N-BEATS architecture achieves an improvement of 11% over baseline statistical models and a 3% enhancement over the previous winner of the M4 competition. These results underscore the robustness of deep learning primitives, like residual blocks, in handling a wide range of forecasting tasks without deep domain-specific tailoring.

Detailed empirical results illustrate this performance leap. Notably, in the M4 dataset, N-BEATS achieved an Overall Weighted Average (OWA) metric of 0.795 compared to 0.821 for the previous best model, cementing its prowess in handling this diverse set of 100,000 time series. A similar trend of superior performance was observed in the M3 and tourism datasets, further adding to the empirical weight of this paper.

Interpretable Deep Learning for Time Series

A significant theoretical and practical advancement introduced by this paper is the interpretable nature of the proposed model. The architecture can be configured to produce interpretable components of the forecast, such as trends and seasonalities, without a substantial loss in accuracy. This is achieved by encoding prior knowledge about time series decomposition directly into the network's structure.

For instance, the model can employ polynomial bases to capture trends and Fourier bases for seasonal components, thus allowing practitioners to dissect the predictions into meaningful components. This interpretability is important for practical adoption as it provides transparency in the model's decisions, aligning with practices in classical TS analysis, like the "seasonality-trend-level" approach (STL).

Architecture and Methodology

N-BEATS is built around a novel hierarchical doubly residual topology. This involves residual connections for both backcast (input reconstructions) and forecast paths, enabling a deep architecture that mitigates the issue of vanishing gradients. The doubly residual stacking principle ensures that each block refines the input signal and contributes to the final forecast in a cumulative manner.

The architecture comprises two main configurations:

1. Generic Configuration (N-BEATS-G): This model does not use any TS-specific input features or scaling methods, relying purely on deep learning components. This generic approach highlights the flexibility and potential of deep learning in TS forecasting.
2. Interpretable Configuration (N-BEATS-I): Here, the model explicitly decomposes forecasts into interpretable parts by enforcing specific bases in the network. For example, a trend stack might use polynomial bases while a seasonality stack employs Fourier bases.

Ensembling is another crucial aspect of the architecture. The final model's performance is bolstered by aggregating several individual models trained with different initializations and on varying loss functions (sMAPE, MAPE, MASE), reflecting the multi-scale and diverse nature of time series data.

Implications and Future Directions

The significant performance gains and the interpretability offered by N-BEATS have both practical and theoretical implications. Practically, the model provides a robust tool for businesses and researchers to enhance forecasting accuracy without the need for domain-specific adjustments. This can translate into more efficient inventory management, financial planning, and strategic business decisions, where even minor improvements in forecasting accuracy can have substantial financial impacts.

Theoretically, the paper challenges the prevailing notion that combining statistical methods with machine learning is necessary for top performance in TS forecasting. Instead, it shows that pure deep learning approaches, when architected judiciously, can achieve or surpass the results of hybrid models. This shifts the focus to advancing deep learning techniques and architectures tailor-made for forecasting tasks.

Future research could extend these findings by exploring the broader applicability of the N-BEATS architecture to multivariate time series data and real-time forecasting scenarios. Additionally, investigating the model's integration with reinforcement learning and transfer learning paradigms could further push the boundaries of what deep learning can achieve in time series analysis.

In conclusion, the N-BEATS paper presents a compelling advancement in the field of time series forecasting, showcasing the power of deep learning in this domain while providing avenues for interpretable and highly accurate forecasting models.