arXiv research

Adaptive ensemble improves flu forecasts with minimal data.

New method uses Transformers for flu forecasting.

Flusion combines multiple data sources to improve flu forecasts.

This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series data. The method is appropriate for jointly modeling hundreds of time series with widely varying, non-stationary dynamics. Given a collection of $N$ time series, the Bayesian …

GRUs predict flu at state and city levels with lower error.

Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures. Internet-based approaches for surveillance are appealing logistically as well as economica…

Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…

We study the problem of distinguishing between two distributions on a metric space; i.e., given metric measure spaces $({\mathbb X}, d, μ_1)$ and $({\mathbb X}, d, μ_2)$, we are interested in the problem of determining from finite data whether or not $μ_1$ is $μ_2$. The key is to use pairwise distances between observat…

We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multi-way data. This generalizes existing work on sparse tensor decomposition and penalized matrix decompositions, in a manner parallel to the generalized lasso for regression and smoothing problems. Our ap…

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistica…

Describes state variables in sequential decision problems, linking them to Markovian and non-Markovian models.

Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression models, which allow an unknown p …

Optimal reconciliation keeps some forecasts unchanged in hierarchical forecasting.

Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improve the reliability and economics of system operation performances. This…

Combining forecasts of 16 ED causes improves accuracy and stability.

Conditional forecasts improve performative prediction accuracy.

A new method improves estimation of COVID-19 vaccine effectiveness.

Study improves seasonal forecasts using deep learning.

For2For combines forecasts to improve time series forecasting.

Two new methods improve forecasting of functional time series data.

Deep learning improves time series forecasting, outperforming other methods.

Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using for…

MPANF improves naive forecast by incorporating directional information.

Simplifies forecast combination by using diversity of out-of-sample forecasts.

Develops forecast hedging for improved calibration of forecasts.

Microdata improves inflation forecasts after major shocks, study finds.

Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In o…

Paper proposes a new method for selecting the best hierarchical forecasting approach.

New algorithm classifies and generates genomic sequences using RG-flow categorifier.

Proposes a neural network for accurate and reconciled hierarchical time series forecasting.

The key contribution of this paper is to propose a classification into two dimensions of the load forecasting studies to decide which forecasting tools to use in which case. This classification aims to provide a synthetic view of the relevant forecasting techniques and methodologies by forecasting problem. In addition,…

This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.

Deep neural networks improve forecasting of non-stationary time-series data.

New model forecasts VaR using quantile CNNs.

This paper improves forecast stability without sacrificing accuracy using dynamic loss weighting.

A new framework detects forecast model inadequacies using online monitoring of forecast errors.

Archive of 20 time series datasets for forecasting evaluation.

As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model w…

Develops a method to ensure accurate quantile forecasts across multiple levels.

A new hierarchical forecasting method using machine learning improves forecast accuracy.

Viral zoonoses have emerged as the key drivers of recent pandemics. Human infection by zoonotic viruses are either spillover events -- isolated infections that fail to cause a widespread contagion -- or species jumps, where successful adaptation to the new host leads to a pandemic. Despite expensive bio-surveillance ef…

New method evaluates language model forecasters by checking consistency of predictions.

The paper proposes a method to improve forecast combination accuracy using portfolio theory.

EasyTime simplifies time series forecasting for researchers and practitioners.

New method predicts wave height exceedance probabilities.

Deep learning improves PV generation quantile forecasting.

Framework improves ETF volatility forecasting by adapting to market conditions.