Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research primarily focuses on examining the impact of specific hyperparameters related to time series, such as context length and validation strategy, on the performance of the state-of-the-art MLP model in time series forecasting. We have conducted a comprehensive series of experiments involving 4800 configurations per dataset across 20 time series forecasting datasets, and our findings demonstrate the importance of tuning these parameters. Furthermore, in this work, we introduce the largest metadataset for timeseries forecasting to date, named TSBench, comprising 97200 evaluations, which is a twentyfold increase compared to previous works in the field. Finally, we demonstrate the utility of the created metadataset on multi-fidelity hyperparameter optimization tasks.

翻译：时间序列预测旨在通过分析历史趋势与模式来预测未来事件。尽管已有深入研究，但深度学习在时间序列预测中应用时的某些关键方面仍存在模糊性。本研究重点考察与时间序列相关的特定超参数（如上下文长度和验证策略）对当前最先进的MLP模型在时间序列预测中性能的影响。我们基于20个时间序列预测数据集，对每个数据集进行了包含4800种配置的全面实验，结果表明调优这些参数至关重要。此外，本文首次引入了时间序列预测领域规模最大的元数据集TSBench，该数据集包含97200次评估，规模较此前研究增长20倍。最后，我们验证了该元数据集在多保真超参数优化任务中的实用价值。