Monte Carlo Analysis for Investment Risk Assessment

Discover how Monte Carlo analysis helps investors assess risk and make informed decisions. Explore its role in generating ...
IEEE

Variable-Dynamic Multivariate Time-Series Forecasting for IoT Systems

Abstract: The past decade has witnessed the success of deep learning-based multivariate time-series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a ...
GitHub

AERCA: Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery (ICLR 2025 Oral)

The AERCA algorithm performs robust root cause analysis in multivariate time series data by leveraging Granger causal discovery methods. This implementation in PyTorch facilitates experimentation on ...
GitHub

Multivariate analysis of ordinal data with missing values and latent variables

Halva—‘grapHical Analysis with Latent VAriables’—is a Python package dedicated to statistical analysis of multivariate ordinal data, designed specifically to handle missing values and latent variables ...
SciELO

Factors associated with suicidal Ideation: a block-ordered multivariate modeling approach

Objective To analyze the occurrence of suicidal ideation in an urban population of a large Brazilian municipality, based on block-organized factors. Method A cross-sectional study with a total of ...
Forbes

How To Use AI For Data Analysis: A Step-By-Step Guide

Leveraging AI to help analyze and visualize data gathered from a variety of data sets enables data-driven insights and fast analysis without the high costs of talent and technology. In today's ...
SciELO

Designing an Index for Multi-location Yield Stability Analysis Involving Univariate and Multivariate Methods in Rice ( Oryza sativa L.)

Under different environmental conditions, crop yields differ primarily due to G and E interactions. The Global Rice Array (GRA-IV) is IRRI's fourth flagship project to identify climate-resilient rice ...