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N-BEATS (Neural Basis Expansion Analysis for Time Series) is a deep learning architecture tailored for forecasting time series data. It diverges from traditional methods that depend on statistical techniques or basic machine learning models by employing deep neural networks to directly learn intricate temporal patterns from the data.
Key Components:
- Basis Function Expansion: N-BEATS decomposes the time series into a set of basis functions, where each basis function represents a component of the time series data. These basis functions can capture different temporal patterns such as trends, seasonality, and other variations.
- Interpretable Forecasting: One of the distinguishing features of N-BEATS is its focus on interpretability. By decomposing the time series into interpretable basis functions, N-BEATS provides insights into the underlying patterns driving the data, making it easier for analysts to understand and interpret the forecasting results.
- Neural Network Architecture: N-BEATS employs a deep neural network architecture consisting of multiple blocks, each of which comprises a stack of fully connected layers. These blocks are designed to learn different basis functions and combine them to produce accurate forecasts.
- Stacked Ensemble: N-BEATS uses a stacked ensemble approach to combine the predictions from multiple blocks within the network. By leveraging the diversity of the learned basis functions, the ensemble improves the overall forecasting performance and robustness of the model.
- Training Objective: During training, N-BEATS minimizes a loss function that measures the discrepancy between the predicted and actual values of the time series. This training objective guides the model to learn the underlying patterns and relationships in the data, enabling it to make accurate forecasts.
- Scalability and Efficiency: N-BEATS is designed to be scalable and efficient, allowing it to handle large-scale time series datasets with millions of data points efficiently. This scalability makes N-BEATS suitable for a wide range of forecasting applications, including those in finance, retail, and energy markets.
Overview
This research proposes a deep neural network architecture for univariate time series point forecasting. The architecture utilizes backward and forward residual links with a deep stack of fully-connected layers. It boasts several advantages: interpretability, applicability to various domains without modification, and fast training times.
The proposed architecture is tested on established datasets (M3, M4, TOURISM) encompassing diverse domains. It achieves state-of-the-art performance in two configurations for all datasets, surpassing a statistical benchmark by 11% and last year’s M4 competition winner (a domain-specific hybrid model) by 3%.
Interestingly, the first configuration doesn’t employ any time-series-specific components. Its strong performance across heterogeneous datasets suggests that, contrary to conventional wisdom, deep learning elements like residual blocks might be sufficient for solving a wide range of forecasting problems on their own.
Finally, the research demonstrates how the architecture can be enhanced to provide interpretable outputs without significantly sacrificing accuracy.
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.
Here is a link to the project.
Overall, N-BEATS represents a powerful framework for time series forecasting that combines the flexibility of deep learning with the interpretability of basis function expansion, enabling analysts to extract valuable insights from time series data and make accurate predictions.