Learn how to understand and compute line integrals in vector fields using both Python and traditional paper methods! This video walks you step by step through the concepts of line integrals, ...

Abstract: Anomaly detection in multivariate time series (MTS) is crucial in domains such as industrial monitoring, cybersecurity, healthcare, and autonomous driving. Deep learning approaches have ...

├── src/ # Source code │ ├── data_utils.py # Data generation and loading utilities │ ├── models.py # Time series forecasting models │ ├── visualization.py # Visualization utilities │ ├── main.py # ...

Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local ...

ABSTRACT: This study develops and empirically calibrates the Community-Social Licence-Insurance Equilibrium (CoSLIE) Model, a dynamic, multi-theoretic framework that reconceptualises ...

This paper proposes a “quasi-agnostic” sign restriction procedure to identify structural shocks in frequentist structural vector autoregression (SVAR) models. It argues that low acceptance rates, ...

Soon to be the official tool for managing Python installations on Windows, the new Python Installation Manager picks up where the ‘py’ launcher left off. Python is a first-class citizen on Microsoft ...

Objective: This study aimed to develop depression incidence forecasting models and compare the performance of autoregressive integrated moving average (ARIMA) and vector-ARIMA (VARIMA) and temporal ...

Abstract: As an efficient recurrent neural network (RNN), reservoir computing (RC) has achieved various applications in time-series forecasting. Nevertheless, a poorly explained phenomenon remains as ...