Modernizing legacy data systems is no longer optional—it's the key to unlocking AI’s full potential with real-time insights, ...

Accurately predicting medium-term electricity usage is essential for effective planning, pricing strategies, and third-party contract negotiations in the electricity generation industry. However, ...

The embedded Python Processing Engine in InfluxDB 3 allows developers to write Python code that analyzes and acts on time series data in real time.

Methods Study design, setting, and data sources This study was a cross-sectional, time series analysis using nationally representative population-based household surveys conducted by the Australian ...

Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series ...

Compared to cross-sectional analyses, showing global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over ...

2 Methods 2.1 Model formulation 2.1.1 Original model for cross-sectional data Here, we briefly present the original normative model [3], developed to be trained and used on cross-sectional data. In ...

Transformer-based models, especially PatchTST, have demonstrated remarkable success in time series forecasting tasks. However, the unique nature of time series data, which often contains jitter, and ...

Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting (ICLR 2023) This is the origin Pytorch implementation of Crossformer: Transformer Utilizing Cross ...

Although cross-sectional analysis is seen as the opposite of time series analysis, the two are used together in practice.