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Found 7 papers, 2 papers with code
Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey
Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems.
Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting
Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts.
Combining Embeddings and Fuzzy Time Series for High-Dimensional Time Series Forecasting in Internet of Energy Applications
The prediction of residential power usage is essential in assisting a smart grid to manage and preserve energy to ensure efficient use.
High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series
In Internet of things (IoT), data is continuously recorded from different data sources and devices can suffer faults in their embedded electronics, thus leading to a high-dimensional data sets and concept drift events.
Applying Genetic Programming to Improve Interpretability in Machine Learning Models
Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning.
BIG-bench Machine Learning
Explainable artificial intelligence
+1
Forecasting in Non-stationary Environments with Fuzzy Time Series
In this approach, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series.
Scalable and Customizable Benchmark Problems for Many-Objective Optimization
It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features.
