KPI prediction dataset

KPI prediction, which is categorized under time series data modeling, serves as a crucial area of investigation within the realm of complex industrial processes. This field focuses on forecasting key performance indicators that are pivotal for assessing the operational efficiency and productivity of industries. By leveraging historical data trends, KPI prediction aids in optimizing process controls and decision-making strategies, thus enhancing overall performance and competitive edge. Advanced techniques, including statistical methods, machine learning algorithms, and deep learning frameworks, are employed to deal with the inherent challenges of time series data such as seasonality, trend decomposition, and noise management. The accuracy of KPI predictions can significantly influence strategic planning and resource allocation, making it a vital tool for managers and analysts aiming to improve outcomes in dynamic and often unpredictable industrial environments.

This dataset is a time series prediction dataset, consisting of 6 sub datasets, each corresponding to different noise levels.

This dataset can be used to verify the performance of different temporal models.

Especailly, it can also be used to validate the performance of the PTF-ED model for the KPI advance prediction.

This dataset also can be used to compare and validate the performance of different KPI early prediction models.