.png){kind=icon}
.png){kind=icon}
Return
River Stage Forecasting using Enhanced Partial Correlation Graph
.png){kind=icon}
PRE
AI
DOI：10.1007/s11269-021-02933-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynamics of different water flow scenarios, which is not scalable for a large network of sensors. This paper presents a novel approach to model spatio-temporal dependencies across river network stations using a partial correlation graph. We also provide a method to enrich this partial correlation graph by eliminating the spurious correlations. We demonstrate the utility of enriched partial correlation graphs in multivariate forecasting for various scenarios and state-of-the-art multivariate forecasting models. We observe that the forecasting techniques that use information from the enriched partial correlation graph outperform standard time series forecasting approaches for river network forecasting.
Keywords:
River stage forecasting
River networks
ARIMA
Graph analysis
Timeseries forecasting
Partial correlation graph
Journal
IF:
4.7
Papers:
8.1K
Citations:
1.6W