yesanton / Process-Sequence-Prediction-with-A-priori-knowledge
Leveraging A-priori Knowledge in Predictive Business Process Monitoring
☆10Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for Process-Sequence-Prediction-with-A-priori-knowledge
- Learning Accurate Generative Models of Business Processes With LSTM Neural Networks☆28Updated 7 months ago
- ☆101Updated 5 years ago
- https://arxiv.org/abs/2009.01561☆22Updated last year
- Multi view deep learning based approach for next activity prediction☆13Updated last year
- Code for the paper "LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances"☆9Updated 6 years ago
- Comparative experimental evaluation of outcome-oriented predictive monitoring techniques on a benchmark consisting of 24 real-world datas…☆32Updated 3 years ago
- Transformer Network for Predictive Business Process Monitoring Tasks☆38Updated last month
- Scripts related to predictive business process monitoring framework with structured and unstructured (textual) data.☆9Updated 6 years ago
- ☆19Updated 2 years ago
- ☆11Updated 3 years ago
- ☆11Updated 3 years ago
- A Business Processes and Logs Generator☆32Updated 9 months ago
- A Deep Learning model for business process predictions. Preprint on arXiv: https://arxiv.org/abs/2102.07838☆11Updated 3 years ago
- Repository containing all implementations and experiments for the EDBN model.☆17Updated 3 years ago
- Automatic process simulation using Simpy and Pm4py.☆16Updated 3 years ago
- This is the complementary code repository for the BINet papers.☆23Updated 3 years ago
- ☆11Updated 3 years ago
- DEBS 2021: Graph Stream Analytics tutorial☆11Updated 3 years ago
- Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification☆95Updated 9 months ago
- ☆11Updated 2 years ago
- (Python, R) Cost-sensitive multiclass classification (Weighted-All-Pairs, Filter-Tree & others)☆47Updated 5 months ago
- Benchmark evaluation for predictive monitoring of remaining cycle time of business processes☆13Updated 2 years ago
- ☆58Updated last week
- The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).☆57Updated last year
- Code for the paper "Improving Missing Data Imputation with Deep Generative Models"☆32Updated 5 years ago
- Code used in the paper "Time Series Clustering via Community Detection in Networks"☆37Updated 4 years ago
- Recurrent Neural Network Implementations for Time Series Forecasting☆72Updated 2 years ago
- Node Embeddings in Dynamic Graphs☆54Updated 2 years ago
- Time-Series Clustering: Overview, R-packages☆37Updated 5 years ago
- A rule-based aproach to explain the output of any machine learning model☆12Updated 7 months ago