… In our work, we extend the graph attention network with IMV-LSTM to capture … GeoMAN
is a multi-level attention-based recurrent neural network for geo-sensory time series prediction…

… an improved GeoMAN (Geo-sensory Multi-level Attention Networks) … time series features
and study the time series prediction … improved multilayer attention mechanism GeoMAN model. …

… In this paper, a time series prediction neural network based on multi-level attention
mechanism has been proposed. In the … GeoMAN: Multi-level Attention Networks for Geo-sensory …

… multi-level attention network, GeoMAN [16], can further improve the performance by modeling
dynamic spatio-temporal … sudden rise-and-fall in geo-sensory time series, ie, what is the …

… of WF which is a type of multivariate time series prediction. Suppose there are N l AWSs, …
apply the same strategy as GeoMAN to combine c t with d t and make the final forecast with: …

… sequence and apply time series methods for prediction. These methods ignore the correlations
… Zheng, “Geoman: Multilevel attention networks for geo-sensory time series prediction,” in …

… Multi-level attention networks for geo-sensory time series (GeoMAN) [14]similarly has two
stages of attention, … Spatiotemporal attention for multivariate time series prediction (STAM) [9] …

… time series and utilize LSTM directly to encode self-temporal … spatio-attention embedded
recurrent neural network is … GeoMAN [2]: Multi-level Attention Network is encoder and …

… Considering the multi-scale nature of geo-sensory data such as air pollution signal, in this
paper we adopt a multi-level … Geoman: Multi-level attention networks for geosensory time …

… Multi-level attention networks for geo-sensory time series (GeoMAN) [14]similarly has two
stages of attention, … Spatiotemporal attention for multivariate time series prediction (STAM) [9] …