This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models. We start with the datasets of different issues and apply pre-processing steps to clean and engineer meaningful representations of time-series data. To identify robust training configurations, we introduce a novel mechanism of multiple cross-validation strategies. We apply different evaluation metrics to find the best-suited models for varying applications. One of the referent applications is our participation in the intelligent forecasting competition held by the United States Agency of International Development (USAID). Finally, we leverage the flexibility of the framework by applying different evaluation metrics to assess the performance of the models in inventory management settings.

翻译：本文提出一种通过精简开发流程核心组件间连接来构建预测模型的框架。该框架支持快速稳健地整合新数据集、实验不同算法以及选择最优模型。我们首先处理不同问题的数据集，应用预处理步骤清理时间序列数据并构建有意义的特征表示。为识别稳健的训练配置，我们引入一种新型多重交叉验证策略。采用多种评估指标为不同应用场景寻找最适模型。参考应用之一是我们参与美国国际开发署(USAID)举办的智能预测竞赛。最终，我们通过应用不同评估指标评估模型在库存管理场景中的性能，充分利用了该框架的灵活性。