We propose a generic 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 to better inform 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. Unlike the other approaches, the long short-term memory (LSTM) network outperforms the other approaches and detects the upcoming high-risk periods with a balanced accuracy of 0.86, confirming the robustness of our methodology and demonstrating that a binary time series model can anticipate these critical periods based on safety inspections. The proposed methodology converts routine safety inspection data into clear weekly risk scores, detecting the periods when accidents are most likely. 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：我们提出了一种通用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建模为二元时间序列。该方法生成每日预测结果，随后汇总为每周安全评估，以便更好地为决策提供信息。为确保预测的可靠性和操作适用性，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程序，并结合基于聚合周期级指标的评价方法。在此框架内，我们对包括逻辑回归、基于树的模型和神经网络在内的多种机器学习算法进行了训练和系统比较。与其他方法不同，长短期记忆（LSTM）网络表现最优，其检测即将到来的高风险时段的平衡准确率达到0.86，这证实了我们方法的稳健性，并证明二元时间序列模型能够基于安全检查数据预测这些关键时段。所提出的方法将常规安全检查数据转化为清晰的每周风险评分，从而识别事故最可能发生的时段。决策者可将这些评分整合至其规划工具中，用于划分检查优先级、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，实现安全投资的最大回报。