DSS4EX: A Decision Support System framework to explore Artificial Intelligence pipelines with an application in time series forecasting

Understanding complex Artificial Intelligence (AI) pipelines for time series forecasting can be challenging for both experts and non-experts. This work introduces DSS4EX, a Decision Support System (DSS) framework designed to facilitate the exploration and comprehension of AI pipelines. The framework is demonstrated through a software demo currently in a prototype phase, specifically tailored for electricity demand forecasting that utilizes the Decomposition-Residuals Deep Neural Network (DR-DNN) pipeline. The dataset used in the software demo, spanning March 18th, 2017, to February 16th, 2021, contains seven hourly time series from an unknown location. It covers 34,360 timesteps of data, including power demand, air pressure, cloud coverage, humidity, temperature, wind direction, and wind speed. The prototype software demo was created to showcase the usability of the DSS4EX framework, allowing users to interactively configure, visualize, and understand the AI pipeline. This interactive approach enhances user engagement and comprehension, promoting informed decision-making. The DSS4EX framework makes complex models more transparent and accessible, bridging the gap between advanced AI models and user understanding. This research demonstrates the potential of DSS4EX in supporting further advancements in AI applications by making sophisticated AI techniques more understandable and usable for a broader audience.