Learn how to use Python as a vector calculator for electric fields. This video shows how to represent charges and position vectors, compute field direction and magnitude, and solve problems faster ...

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 ...

Matt Katz (left) and Jake Welty (right) reveal the one-of-one Vector M12 racecar. Video by Drew Manley (Cooled Collective) In a nondescript storage facility on the outskirts of Los Angeles, a small ...

├── 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 ...

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, ...

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 ...

One of the core problems with AI is the notoriously high power and computing demand, especially for tasks such as media generation. On mobile phones, when it comes to running natively, only a handful ...

ABSTRACT: To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models.

Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and are very strong at ...