Time series anomaly detection
- Anomaly detection
Detection of anomalies in time series of cyclic nature data.
To develop the algorithm our developers have used an algorithm of confidence bands estimation with L2 metrics.
The algorithm was suggested in the article “Confidence bands for time series data” by J. Korpela, K. Puolamaki and A. Gionis.
The mentioned algorithm was modified to track the seasonal factor in time series: for that purpose several variants of data preprocessing were suggested and tested; the cross-validation system for optimum training parameters selection based on the specifics of the client's system was also realized.
To demonstrate the results data visualization was implemented.
Model for restoring blurred and pixelated faces in a photo.
Workout helper app
Mobile app for the estimation of proper body positions during the workout.
Car rental price simulation and prediction
The goal of the project is to train models for car rental price prediction in Japan based on the prices and demand for car rental from some Japanese car rental companies and the history of weather data in Japan.