arXiv research

Generalizes memory and forecasting capacities for nonlinear recurrent networks with dependent inputs.

Managing data storage growth is of crucial importance to businesses. Poor practices can lead to large data and financial losses. Access to storage information along with timely action, or capacity forecasting, are essential to avoid these losses. In addition, ensuring high accuracy of capacity forecast estimates along …

A latent function decomposition method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses the Multi-Output Gaussian Process, a generative machine learning framework for multi-task and transfer learning. The MCGP decomposes the available capacity trends from multiple battery cells int…

New measure predicts Dutch housing market downturns.

Study optimizes pricing under uncertainty and capacity constraints.

Study characterizes memory capacity of quantum reservoirs using transmon qubits.

India's 2020-21 GDP growth forecast is projected at 1.9% due to COVID-19.

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent…

New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.

Study builds dataset and benchmarks ML models for accurate solar and wind power forecasting in France.

Model forecasts water demand with probabilistic multi-step-ahead approach.

Robust forecast framework reduces distribution error by 63%.

A new data-driven model forecasts electricity prices efficiently.

A new LSTM architecture improves time series forecasting efficiency.

AI helps forecasters understand TC convective evolution before intensification.

N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.

Study finds macroeconomic indicators predict health workforce and infrastructure measures.

ChatGPT predicts stock market reactions from news headlines without financial training.

Paper presents a method for probabilistic load forecasting using adaptive online learning.

Proposes a transformer model with geostatistical inductive bias for spatio-temporal forecasting.

Signature kernel scoring rule improves weather forecasting by capturing temporal and spatial dependencies.

Short-term probabilistic forecasting of German electricity imbalance prices.

Improved NODEs for long-term time series forecasting.

SAMformer improves transformer performance in time series forecasting.

The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limited by sparsely deployed weather balloons. We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor device…

Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architecture is time-invaria…

Paper tackles inventory management with deep learning, improving performance and adherence to constraints.

Proposes a Big Data framework for SC forecasting, including data preprocessing and machine learning.

ERDM integrates rolling forecasts with diffusion models for complex dynamics.

Exponentially smoothed RNNs improve industrial forecasting.

DEPTS learns to forecast periodic time series with improved accuracy.

This paper forecasts renewable energy prospects in South America through cross-border interconnection.

The paper optimizes forecasting for risk-adjusted decisions under trading frictions.

New method improves wind and solar energy forecasts by 48 hours.

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with ti…

A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.

Research focuses on predicting electricity prices with complex models considering probabilistic forecasts.

This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction. To handle this challenging task, we introduce DILATE (DIstortion Loss including shApe and TimE), a new objective function for training deep neural networks. DILATE aims at accurately predicting s…

The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop m…

LSTM networks improve stock price prediction accuracy.

PT network optimizes asset weights without forecasting returns.

DRN improves actuarial distributional forecasting with interpretable neural networks.

Forecast-to-fill strategy generates durable alpha in gold futures.

CPAS uses machine learning to plan hospital resources for COVID-19.

New method improves traffic forecasting models by adapting to spatial shifts.

ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.

This paper improves stock price prediction using multimodal data.

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level …