ForecastPFN: Synthetically-Trained Zero-Shot Forecasting (2311.01933v1)

Abstract: The vast majority of time-series forecasting approaches require a substantial training dataset. However, many real-life forecasting applications have very little initial observations, sometimes just 40 or fewer. Thus, the applicability of most forecasting methods is restricted in data-sparse commercial applications. While there is recent work in the setting of very limited initial data (so-called `zero-shot' forecasting), its performance is inconsistent depending on the data used for pretraining. In this work, we take a different approach and devise ForecastPFN, the first zero-shot forecasting model trained purely on a novel synthetic data distribution. ForecastPFN is a prior-data fitted network, trained to approximate Bayesian inference, which can make predictions on a new time series dataset in a single forward pass. Through extensive experiments, we show that zero-shot predictions made by ForecastPFN are more accurate and faster compared to state-of-the-art forecasting methods, even when the other methods are allowed to train on hundreds of additional in-distribution data points.

• The paper introduces ForecastPFN, a synthetically-trained zero-shot forecasting model that leverages offline synthetic data to approximate Bayesian inference.
• It employs a transformer-based, encoder-only architecture with dual encoders to capture diverse time-frequency components effectively.
• The model outperforms existing methods by delivering precise predictions in 0.2 seconds, dramatically reducing computational runtime.

Insights into Synthetically-Trained Zero-Shot Forecasting: An Analysis of ForecastPFN

Time-series forecasting, an enduringly pertinent field, intersects many domains, such as healthcare, economics, and climate science. The challenge commonly faced is the scarcity of initial data, often with time-series containing as few as 40 points or less. Such limitations hinder the applicability of many conventional and contemporary forecasting methods, which typically depend on substantial training datasets. This paper presents ForecastPFN, a pioneering model that advances the capabilities of zero-shot forecasting using a synthetically trained approach, eschewing dependency on large training datasets.

ForecastPFN is unique in that it leverages a synthetic data distribution that mirrors a broad spectrum of real-world time series. The innovation lies in its use of a prior-data fitted network (PFN), a method pre-trained offline on synthetic data to approximate Bayesian inference. The training process involves creating a time-series dataset with embedded trends—linear and exponential—and seasonal patterns that are derived from comprehensive parameters including daily, monthly, and yearly scales. These are compounded by a noise model based on a Weibull distribution, which showcases flexibility by varying coefficients and maintaining the noise’s expected value at unity.

The Authors' experiments critically illustrate that ForecastPFN exceeds the performance of state-of-the-art forecasting methods without additional real-world training data. For instance, when compared to leading alternatives—such as FEDformer, Informer, and Autoformer—ForecastPFN not only surpassed these methods in forecast accuracy but also demonstrated extraordinary computational efficiency. Specifically, while existing methodologies demanded extensive and costly computational processes beyond 100 times longer runtime, ForecastPFN could generate precise predictions in a mere 0.2 seconds through a single model pass.

In assessing the architectural foundation of ForecastPFN, it utilizes a transformer model modified into an encoder-only variant, comprising two encoders to handle heterogeneous time-frequency components effectively. Its structure accommodates diverse dataset scales via innovations like robust scaling and modular synthetic modeling, tuning the design toward generalization rather than overfitting.

The implications of such a model are extensive. From a theoretical perspective, ForecastPFN challenges traditional data-intensive paradigms by demonstrating viable forecasting through purely synthetic data. Practically, it opens vast opportunities for industrial applications where data collection might be expensive or infeasible, such as nascent market cycles or emerging IoT ecosystems where time-series is sparse but business-critical.

Future research could expand ForecastPFN’s capacity to multivariate time series, enabling a broader range of applications such as co-dependent sensory data and multi-echelon supply chain forecasting. There is also room to improve and integrate probabilistic predictions to enhance decision-making capabilities. Furthermore, exploring synergy with large pre-trained models remains an intriguing path, potentially expanding the foundation model capabilities for time-series forecasting.

Overall, ForecastPFN represents an academically rigorous, data-efficient, and practically powerful advancement in time-series forecasting. Its contribution is significant, given its transformative potential to enable robust forecasting capabilities within data-constrained scenarios, facilitating encompassing forecasting solutions across different scientific and commercial fields.