Machine learning models have made significant progress in load forecasting,
but their forecast accuracy is limited in cases where historical load data is
scarce. Inspired by the outstanding performance of large language models (LLMs)
in computer vision and natural language processing, this paper aims to discuss
the potential of large time series models in load forecasting with scarce
historical data. Specifically, the large time series model is constructed as a
time series generative pre-trained transformer (TimeGPT), which is trained on
massive and diverse time series datasets consisting of 100 billion data points
(e.g., finance, transportation, banking, web traffic, weather, energy,
healthcare, etc.). Then, the scarce historical load data is used to fine-tune
the TimeGPT, which helps it to adapt to the data distribution and
characteristics associated with load forecasting. Simulation results show that
TimeGPT outperforms the benchmarks (e.g., popular machine learning models and
statistical models) for load forecasting on several real datasets with scarce
training samples, particularly for short look-ahead times. However, it cannot
be guaranteed that TimeGPT is always superior to benchmarks for load
forecasting with scarce data, since the performance of TimeGPT may be affected
by the distribution differences between the load data and the training data. In
practical applications, we can divide the historical data into a training set
and a validation set, and then use the validation set loss to decide whether
TimeGPT is the best choice for a specific dataset.

研究表明在历史电负荷数据有限的情况下，机器学习模型在负荷预测中的准确性受到限制。该论文探讨了大规模时间序列模型（TimeGPT）在历史数据有限的负荷预测中的潜力，并通过训练模型适应数据分布和特征，取得了在短期预测中超越其他机器学习模型和统计模型的成果。然而，由于负荷数据与训练数据之间的分布差异可能会影响 TimeGPT 的性能，因此在实际应用中需要使用验证集损失来确定是否选择 TimeGPT。

在负荷预测中的 TimeGPT：一个大型时间序列模型视角

TimeGPT in Load Forecasting: A Large Time Series Model Perspective

In this paper, we introduce TimeGPT, the first foundation model for time
series, capable of generating accurate predictions for diverse datasets not
seen during training. We evaluate our pre-trained model against established
statistical, machine learning, and deep learning methods, demonstrating that
TimeGPT zero-shot inference excels in performance, efficiency, and simplicity.
Our study provides compelling evidence that insights from other domains of
artificial intelligence can be effectively applied to time series analysis. We
conclude that large-scale time series models offer an exciting opportunity to
democratize access to precise predictions and reduce uncertainty by leveraging
the capabilities of contemporary advancements in deep learning.

TimeGPT 是时间序列的首个基础模型，能够生成准确的预测结果，我们通过评估其与统计学、机器学习和深度学习方法的性能对比发现，TimeGPT 在性能、效率和简洁性方面表现出色，研究结果显示了人工智能其他领域的洞见能够有效应用于时间序列分析，大规模时间序列模型为普遍获取精确预测和降低不确定性提供了激动人心的机会。