  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.
