A combination of LSTM and EDM models to address the issue of anomaly classification and prediction in time series data. Working with sensor data of forklifts used in storage and retrieval systems. Predictors based on variance and median methods in the handling of anomalies.
Predict machine components failure in order to apply predictive maintenance to robot forklifts using historical data. Train classification model to improve performance of outlier and breakout detection.
- LSTM
- Outlier Detection
- Data Visualization
- Python
- Pandas, jupyter
- LSTM
- data exploration
- data cleaning
- reporting
| Name |
|---|
| william ardianto |
| teoh kenghooi |
| narjes khatoon |
| yee xunwei |
| philip khor |