EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
Channel: BIT_TUMCS
Date: 05/10/2021

KI2021 Talk about EVARS-GPR

Link to Paper: https://doi.org/10.1007/978-3-030-87626-5_11

Link to Code: https://github.com/grimmlab/evars-gpr

References:
(1) Aminikhanghahi, S., Cook, D.J.: A Survey of Methods for Time Series Change Point Detection. Knowledge and information systems 51(2), 339–367 (2017).

(2) Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and practice, 2nd edn. (2018)

(3) Ni, W., Tan, S.K., Ng, W.J., Brown, S.D.: Moving-Window GPR for Nonlinear Dynamic System Modeling with Dual Updating and Dual Preprocessing. Ind. Eng. Chem. Res. 51(18),  6416–6428 (2012).

(4) Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis. Forecasting and control. Wiley Series in Probability and Statistics. John Wiley & Sons Inc, Hoboken, New Jersey (2016)

(5) Earth System Research Laboratory: CO2 PPM - Trends in Atmospheric Carbon Dioxide. Atmospheric Carbon Dioxide Dry Air Mole Fractions at Mauna Loa, Hawaii. https://datahub.io/core/co2-ppm-daily#data. Accessed 14 May 2021