This journal focuses on the application of data science and machine learning techniques to address complex problems across various scientific disciplines. It features research on developing novel algorithms for prediction, classification, and anomaly detection, often with a focus on uncertainty quantification and model interpretability. The journal also explores the integration of data science with experimental design and the analysis of large-scale, heterogeneous datasets, including time-series and spatio-temporal data.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Subject Classification
Research Topics (OpenAlex)
Statistical Methods and InferenceNeural Networks and ApplicationsComplex Systems and Time Series AnalysisGaussian Processes and Bayesian InferenceFinancial Markets and Investment StrategiesStatistical Methods and Bayesian InferenceFinancial Risk and Volatility ModelingAdvanced Causal Inference TechniquesEnergy Load and Power ForecastingAnomaly Detection Techniques and Applications