We propose a model-agnostic framework for short-term occupational accident forecasting that leverages safety inspections and models accident occurrences as binary time series. The approach generates daily predictions, which are then aggregated into weekly safety assessments for better decision making. To ensure the reliability and operational applicability of the forecasts, we apply a sliding-window cross-validation procedure specifically designed for time series data, combined with an evaluation based on aggregated period-level metrics. Several machine learning algorithms, including logistic regression, tree-based models, and neural networks, are trained and systematically compared within this framework. Across all tested algorithms, the proposed framework reliably identifies upcoming high-risk periods and delivers robust period-level performance, demonstrating that converting safety inspections into binary time series yields actionable, short-term risk signals. The proposed methodology converts routine safety inspection data into clear weekly and daily risk scores, detecting the periods when accidents are most likely to occur. Decision-makers can integrate these scores into their planning tools to classify inspection priorities, schedule targeted interventions, and funnel resources to the sites or shifts classified as highest risk, stepping in before incidents occur and getting the greatest return on safety investments.

翻译：我们提出一种模型无关的短期职业事故预测框架，该框架利用安全检查数据并将事故发生率建模为二元时间序列。该方法生成每日预测结果，随后聚合为每周安全评估以支持更优决策。为确保预测的可靠性与操作适用性，我们采用了专门为时间序列数据设计的滑动窗口交叉验证流程，并结合基于聚合周期级指标的性能评估。在此框架内，我们对包括逻辑回归、树模型及神经网络在内的多种机器学习算法进行了训练与系统比较。所有测试算法均表明，该框架能可靠识别即将到来的高风险时段，并提供稳健的周期级预测性能，这证明将安全检查数据转化为二元时间序列可产生具有可操作性的短期风险信号。本方法将常规安全检查数据转化为清晰的周度与每日风险评分，从而检测事故最可能发生的时段。决策者可将这些评分整合至规划工具中，用以划分检查优先级、安排针对性干预措施，并将资源集中调配至被归类为最高风险的工作场所或轮班时段，从而在事故发生前介入干预，实现安全投资效益的最大化。