Causality indices for bivariate time series data: A comparative review of performance

Discovering Geographical Flock Patterns of CO2 Emissions in China Using Trajectory Mining Techniques

Carbon dioxide (CO₂) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO₂ emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO₂ emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO₂ emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high-low attribute values criterion and the extreme number-duration values criterion. A case study is conducted based on the CO₂ emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO₂ emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction

Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised. Due to these identified cause and effect relationships, T1D management can be examined via the causality context. In this respect, this work initially investigates these relations and quantifies the causality strengths of each variable with BGL using the convergent cross mapping method (CCM). Then, considering the extended CCM, the causality strengths of each variable for different lags are quantified. After that, the optimal time lag for each variable is determined according to the quantified causality effects. Subsequently, the feasibility of leveraging causality information as prior knowledge for BGL prediction is investigated by proposing two approaches. In the first approach, causality strengths are used as weights for relevant affecting variables. In the second approach, the optimal causal lags and the corresponding causality strengths are considered the shifts and weights for the variables, respectively. Overall, the evaluation criteria and statistical analysis used for comparing results show the effectiveness of using causality analysis in T1D management.

Exploring the effect of climate risk on agricultural and food stock prices: Fresh evidence from EMD-Based variable-lag transfer entropy analysis

Climate has traditionally played an important role in the development of countries, owing to its inherent relationship with agricultural output and pricing. This study explores one such association between the most well-known climate anomaly, the El Niño34 Southern Oscillation, and international commodity prices of agriculture and food indexes. This study addresses the potentially causal effect of El Niño34 on international agricultural and food stock prices. To do so, we develop a novel approach: the empirical mode decomposition variable-lag transfer entropy (EMD-VL transfer entropy) by combining the variable-lag transfer entropy framework and the empirical mode decomposition. The evidence reveals the following major results. First, climate shocks affect global agricultural stock prices in the short-term. Second, significant transfer entropy from El Niño34 to food index appeared at mid- and long-term business cycles. Third, unidirectional causal effect from climate shocks to agricultural and food stock prices is more intense in the short business cycle attesting to the impact of climate shocks on the food market, which is especially visible in the short-term horizon. Finally, our proposed method exceeds the traditional variable-lag transfer entropy by detecting such causal interplay at various business cycles, which is useful for investors and policymakers.