Many real-world applications of tabular data involve using historic events to predict properties of new ones, for example whether a credit card transaction is fraudulent or what rating a customer will assign a product on a retail platform. Existing approaches to event prediction include costly, brittle, and application-dependent techniques such as time-aware positional embeddings, learned row and field encodings, and oversampling methods for addressing class imbalance. Moreover, these approaches often assume specific use-cases, for example that we know the labels of all historic events or that we only predict a pre-specified label and not the data's features themselves. In this work, we propose a simple but flexible baseline using standard autoregressive LLM-style transformers with elementary positional embeddings and a causal language modeling objective. Our baseline outperforms existing approaches across popular datasets and can be employed for various use-cases. We demonstrate that the same model can predict labels, impute missing values, or model event sequences.

翻译：许多表格数据的实际应用涉及利用历史事件预测新事件的属性，例如信用卡交易是否欺诈或客户在零售平台上为产品赋予何种评级。现有的事件预测方法包括成本高昂、脆弱且依赖应用场景的技术，例如时间感知位置嵌入、学习的行与字段编码以及用于处理类别不平衡的过采样方法。此外，这些方法通常假设特定的使用场景，例如已知所有历史事件的标签，或仅预测预先指定的标签而非数据特征本身。在本研究中，我们提出了一种简单而灵活的基线方法，该方法采用标准的自回归LLM风格Transformer，配备基础位置嵌入和因果语言建模目标。我们的基线方法在多个常用数据集上优于现有方法，并适用于多种应用场景。我们证明，同一模型能够预测标签、填补缺失值或建模事件序列。