Analyzing the Use of Synthetic Data in Time Series Forecasting

Core Concepts

The author explores the effectiveness of training time series models on synthetic data compared to real-life examples, highlighting the importance of source dataset selection for model performance.

Abstract

The content delves into the debate of training time series models on synthetic data versus real-life examples. It discusses the challenges faced in forecasting large amounts of time series and proposes a foundation model approach. The experiments conducted reveal that using even a limited number of real-time series data yields better results than training on synthetic data. The choice of source dataset significantly impacts model performance during inference.

Stats

The M3 Quarterly dataset has an MSE of 5.171 and an MAE of 5.257.
The M4 Daily dataset has an MSE of 4.638 and an MAE of 3.950.
The M4 Weekly dataset has an MSE of 4.650 and an MAE of 4.550.

Quotes

"In this work, we consider the essential question if it is advantageous to train a foundation model on synthetic data or it is better to utilize only a limited number of real-life examples."
"Our experiments are conducted only for regular time series and speak in favor of leveraging solely the real time series."
"The choice of the proper source dataset strongly influences the performance during inference."

Key Insights Distilled From

Towards Foundation Time Series Model

by Kseniia Kuvs... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02534.pdf

Deeper Inquiries

How can the findings from training on synthetic data versus real-life examples be applied to other machine learning domains

The findings from training on synthetic data versus real-life examples in time series modeling can be applied to other machine learning domains by highlighting the importance of dataset quality and diversity. The experiments conducted in the context above demonstrate that leveraging even a limited number of real-life examples can lead to more favorable results compared to training on synthetic data. This emphasizes the significance of having high-quality, diverse, and representative datasets for model training across various domains. By prioritizing real-world data over synthetic data, machine learning practitioners can ensure that their models are better equipped to handle the complexities and nuances present in actual scenarios.

What are potential drawbacks or limitations when relying solely on synthetic data for time series forecasting

Relying solely on synthetic data for time series forecasting comes with potential drawbacks and limitations. One major limitation is the lack of variability and complexity that may exist in real-world time series data. Synthetic data generation techniques may not fully capture all the intricacies present in actual time series patterns, leading to suboptimal model performance when faced with unseen or unique situations. Additionally, there is a risk of introducing biases or unrealistic assumptions into the model if the synthetic data does not accurately reflect true underlying trends or relationships within the target domain. Furthermore, relying solely on synthetic data may limit the model's ability to generalize well beyond the generated dataset, potentially hindering its effectiveness in practical applications.

How might advancements in generating diverse and representative synthetic series impact future research in time series modeling

Advancements in generating diverse and representative synthetic series have significant implications for future research in time series modeling. By developing more sophisticated methods for creating synthetic time series data with rich properties and large volumes, researchers can address challenges related to limited or inadequate real-world datasets for training foundation models effectively. These advancements open up opportunities for exploring novel approaches to pretraining models using synthetically generated datasets without compromising performance or generalizability. Moreover, improved techniques for synthesizing diverse time series patterns enable researchers to conduct comprehensive comparisons between artificial and natural time series datasets, shedding light on best practices for leveraging different types of data sources in modeling tasks.