Spatio-temporal kriging is an essential research problem in sensor networks due to the sparsity of deployed sensors. While recent studies consider spatial and temporal correlations, they often overlook the sparse spatial distribution of locations and the incomplete features across locations. To tackle these problems, we propose an Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging (AnchorGK). AnchorGK introduces anchor locations to enable effective data stratification for accurate kriging. Anchor locations are constructed based on feature availability, and strata are subsequently established based on the an- chor locations. This stratification serves two purposes: 1) it ensures that the spatial correlations between unknown areas (no observations) and surrounding known locations are accurately represented and dynamically updated within the graph learning framework, and 2) it facilitates the use of all available features across different strata through a novel incremental representation method. Building on the data stratification, we propose a dual-view graph learning layer that integrates information from relevant features and locations and learns distinct representations for different strata. Finally, kriging is performed based on the obtained strata representations. Experimental results on multiple benchmark datasets demonstrate that AnchorGK consistently outperforms existing state-of-the-art methods. Our codes, datasets, and related materials are given in: https://github.com/xren451/Spatial-interpolation

Time series prediction presents a significant challenge across various domains, such as transportation systems, environmental science, and multiple industrial sectors. Real-world time series data commonly exhibit periodic patterns and irregular sampling rates. Recent advancements in long sequence time series forecasting have made significant progress in adopting deep neural networks, particularly the Transformers, renowned for their robust representational capabilities. However, current Transformer-based models consider time steps as discrete tokens, thereby failing to account for periodicity and temporal intervals when selecting relevant time steps in the past. To address this limitation, we propose an end-to-end framework called Periormer for forecasting irregularly sampled time series. Periormer comprises three key components: (1) a novel input embedding layer that encodes the periodicity and time interval information, analogous to positional encoding in Transformers; (2) a feature-wise periodic attention mechanism that selects essential data points considering the periods and amplitudes of the periodic signals; and (3) a cross-feature periodic attention mechanism that identifies essential features relevant to the prediction. Experiments on four real-world datasets and one synthetic dataset demonstrate that Periormer reduces the mean squared error by 14.9% compared to state-of-the-art models.

Missing data imputation for location-based sensor data has attracted much attention in recent years. The state-of-the-art imputation methods based on graph neural networks have a priori assumption that the spatial correlations between sensor locations are static. However, real-world data sets often exhibit dynamic spatial correlations. This paper proposes a novel approach to capturing the dynamics of spatial correlations between geographical locations as a composition of the constant, long-term trends and periodic patterns. To this end, we design a new method called Dynamic Adjacency Matrix Representation (DAMR) that extracts various dynamic patterns of spatial correlations and represents them as adjacency matrices. The adjacency matrices are then aggregated and fed into a well-designed graph representation learning layer for predicting the missing values. Through extensive experiments on six real-world data sets, we demonstrate that DAMR reduces the MAE by up to 19.4% compared with the state-of-the-art methods for the missing value imputation task

Machine Learning typically assumes that training and test sets are independently drawn from the same distribution, but this assumption is often violated in practice which creates a bias. Many attempts to identify and mitigate this bias have been proposed, but they usually rely on ground-truth information. But what if the researcher is not even aware of the bias? In contrast to prior work, this paper introduces a new method, Imitate, to identify and mitigate Selection Bias in the case that we may not know if (and where) a bias is present, and hence no ground-truth information is available. Imitate investigates the dataset's probability density, then adds generated points in order to smooth out the density and have it resemble a Gaussian, the most common density occurring in real-world applications. If the artificial points focus on certain areas and are not widespread, this could indicate a Selection Bias where these areas are underrepresented in the sample. We demonstrate the effectiveness of the proposed method in both, synthetic and real-world datasets. We also point out limitations and future research directions.

Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performance evaluation of an ACO-based algorithm for solving large-scale global optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC'2010 Special Session and Competition on Large-Scale Global Optimization.