Time Series Prediction for IVIS Framework
| Thesis title in Czech: | Predikce časových řad pro IVIS Framework |
|---|---|
| Thesis title in English: | Time Series Prediction for IVIS Framework |
| Key words: | IVIS Framework|ARIMA|IVIS |
| English key words: | IVIS Framework|ARIMA|IVIS |
| Academic year of topic announcement: | 2020/2021 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Department of Distributed and Dependable Systems (32-KDSS) |
| Supervisor: | prof. RNDr. Tomáš Bureš, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 05.02.2021 |
| Date of assignment: | 05.02.2021 |
| Confirmed by Study dept. on: | 19.02.2021 |
| Date and time of defence: | 10.09.2021 09:00 |
| Date of electronic submission: | 22.07.2021 |
| Date of submission of printed version: | 22.07.2021 |
| Date of proceeded defence: | 10.09.2021 |
| Opponents: | doc. RNDr. Jan Kofroň, Ph.D. |
| Guidelines |
| IVIS is a web-based framework that can be used, among other things, to manage and visualize time series data. However, there is currently no built-in support for time series forecasts. This can be addressed to an extent by using IVIS's user-defined tasks (parametrizable Python scripts that operate on data managed by IVIS). While this approach offers great flexibility, it is not very user friendly. In case multiple prediction models are involved, it can quickly become hard to manage.
The goal of this work is to extend IVIS with support for built-in time series prediction models and management of their instances. The extension should build upon the existing task infrastructure already present in IVIS. As a representative of the prediction models, the thesis will integrate the ARIMA forecasting method. The thesis will be evaluated on publicly available real-life data set. |
| References |
| [1] Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. (https://OTexts.com/fpp2)
[2] ReactJS Documentation (https://reactjs.org/docs) [3] statsmodels documentation (https://www.statsmodels.org/stable) [4] pmdarima documentation (https://alkaline-ml.com/pmdarima) [5] IVIS GitHub repository (https://github.com/smartarch/ivis-core) |