Abstract
Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making through anticipatory insights. By accurately predicting future outcomes, the ability to strategize, preemptively address risks, and minimize their potential impact is enhanced. The precision in forecasting spatial and temporal patterns holds significant potential for optimizing resource allocation, land utilization, and infrastructure development. While existing review and survey papers predominantly focus on specific forecasting domains such as intelligent transportation, urban planning, pandemics, disease prediction, climate and weather forecasting, environmental data prediction, and agricultural yield projection, limited attention has been devoted to comprehensive surveys encompassing multiple objects concurrently. This article addresses this gap by comprehensively analyzing techniques employed in traffic, pandemics, disease forecasting, climate and weather prediction, agricultural yield estimation, and environmental data prediction. Furthermore, it elucidates challenges inherent in spatio-temporal forecasting and outlines potential avenues for future research exploration.

Abstract
This study investigates the impact of class imbalance and its potential interplay with other factors on machine learning models for transportation mode classification, utilising two real-world GPS trajectory datasets. A Random Forest model serves as the baseline, demonstrating strong performance on the relatively balanced dataset but experiencing significant degradation on the imbalanced one. To mitigate this effect, we explore various state-of-the-art class imbalance learning techniques, finding only marginal improvements. Resampling the fairly balanced dataset to replicate the imbalanced distribution suggests that factors beyond class imbalance are at play. We hypothesise and provide preliminary evidence for class overlap as a potential contributing factor, underscoring the need for further investigation into the broader range of classification difficulty factors. Our findings highlight the importance of balanced class distributions and a deeper understanding of factors such as class overlap in developing robust and generalisable models for transportation mode detection.

Abstract
As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts' efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.

Abstract
A growing number of organizations are adopting a strategy of breaking down large data analysis problems into specific sub-problems, tailoring models for each. However, handling a large number of individual models can pose challenges in understanding organization-wide phenomena. Recent studies focus on using decision trees to create a consensus model by aggregating local decision trees into sets of rules. Despite efforts, the resulting models may still be incomplete, i.e., not able to cover the entire decision space. This paper explores methodologies to tackle this issue by generating complete consensus models from incomplete rule sets, relying on rough estimates of the distribution of independent variables. Two approaches are introduced: synthetic dataset creation followed by decision tree training and a specialized algorithm for creating a decision tree from symbolic data. The feasibility of generating complete decision trees is demonstrated, along with an empirical evaluation on a number of datasets.

Abstract
In this paper, a solution to monitor the location of humans during their activity in the agriculture sector with the aim to boost productivity and efficiency is provided. Our solution is based on map-matching methods, that are used to track the path spanned by a worker along a specific activity in an agriculture culture. Two different cultures are taken into consideration in this study olives and vines. We leverage the symmetry of the geometry of these cultures into our solution and divide the problem three-fold initially, we estimate a path of a worker along the fields, then we apply the map-matching to such path and finally, a post-processing method is applied to ensure local continuity of the sequence obtained from map-matching. The proposed methods are experimentally evaluated using synthetic and real data in the region of Mirandela, Portugal. Evaluation metrics show that results for synthetic data are robust under several sampling periods, while for real-world data, results for the vine culture are on par with synthetic, and for the olive culture performance is reduced.