A Nonparametric Spatio-temporal SDE Model

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa

Standard

A Nonparametric Spatio-temporal SDE Model. / Yildiz, Cagatay; Heinonen, Markus; Lähdesmäki, Harri.

NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation, 2018. s. 1-5.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa

Harvard

Yildiz, C, Heinonen, M & Lähdesmäki, H 2018, A Nonparametric Spatio-temporal SDE Model. julkaisussa NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation, Sivut 1-5, Montreal, Kanada, 03/12/2018.

APA

Yildiz, C., Heinonen, M., & Lähdesmäki, H. (2018). A Nonparametric Spatio-temporal SDE Model. teoksessa NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada (Sivut 1-5). Neural Information Processing Systems Foundation.

Vancouver

Yildiz C, Heinonen M, Lähdesmäki H. A Nonparametric Spatio-temporal SDE Model. julkaisussa NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation. 2018. s. 1-5

Author

Yildiz, Cagatay ; Heinonen, Markus ; Lähdesmäki, Harri. / A Nonparametric Spatio-temporal SDE Model. NIPS 2018 Spatiotemporal Workshop: 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Neural Information Processing Systems Foundation, 2018. Sivut 1-5

Bibtex - Lataa

@inproceedings{75a5e67643bf410daa0cb88cfb107687,
title = "A Nonparametric Spatio-temporal SDE Model",
abstract = "We propose a nonparametric spatio-temporal stochastic differential equation (SDE) model that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We augment the input space of the drift function of an SDE with a temporal component to account for spatio-temporal patterns. The experiments on a real world data set demonstrate that the spatio-temporal model is better able to fit the data than the spatial model and also reduce the forecasting error.",
keywords = "stochastic differential equation, gaussian processes, spatiotemporal drift",
author = "Cagatay Yildiz and Markus Heinonen and Harri L{\"a}hdesm{\"a}ki",
year = "2018",
language = "English",
pages = "1--5",
booktitle = "NIPS 2018 Spatiotemporal Workshop",
publisher = "Neural Information Processing Systems Foundation",

}

RIS - Lataa

TY - GEN

T1 - A Nonparametric Spatio-temporal SDE Model

AU - Yildiz, Cagatay

AU - Heinonen, Markus

AU - Lähdesmäki, Harri

PY - 2018

Y1 - 2018

N2 - We propose a nonparametric spatio-temporal stochastic differential equation (SDE) model that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We augment the input space of the drift function of an SDE with a temporal component to account for spatio-temporal patterns. The experiments on a real world data set demonstrate that the spatio-temporal model is better able to fit the data than the spatial model and also reduce the forecasting error.

AB - We propose a nonparametric spatio-temporal stochastic differential equation (SDE) model that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We augment the input space of the drift function of an SDE with a temporal component to account for spatio-temporal patterns. The experiments on a real world data set demonstrate that the spatio-temporal model is better able to fit the data than the spatial model and also reduce the forecasting error.

KW - stochastic differential equation

KW - gaussian processes

KW - spatiotemporal drift

UR - https://openreview.net/forum?id=HJzoRCKwjQ

M3 - Conference contribution

SP - 1

EP - 5

BT - NIPS 2018 Spatiotemporal Workshop

PB - Neural Information Processing Systems Foundation

ER -

ID: 31027609