What is the forecast for Ordinals?

To address this challenge, a novel forecasting framework is introduced in this work. This framework combines the strengths of inductive rules and fuzzy logic, two powerful tools in artificial intelligence.

Inductive rules are used to extract meaningful patterns from historical weather data. These rules capture the relationships between different weather attributes and help in understanding how they evolve over time.

Ordinal classification represents a unique approach to predicting weather phenomena, where the output classes follow a natural ordering. This includes predicting various weather attributes like temperature ranges, wind speed, and precipitation levels.

Fuzzy logic, on the other hand, provides a way to handle the inherent imprecision and uncertainty in weather forecasting. It allows for a more nuanced representation of the weather conditions, leading to more accurate predictions.

The challenge in ordinal classification lies in accurately capturing the inherent ordering among the output classes. Traditional classification methods might not be well-suited for this task as they often treat the classes as independent and unordered.