mailitics

Tag: forecasting

  • Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting

    Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting arXiv:2512.04690v1 Announce Type: new Abstract: We present a novel recurrent neural network architecture designed explicitly for day-ahead electricity price forecasting, aimed at improving short-term decision-making and operational management in energy systems. Our combined forecasting model embeds linear structures, such as expert models and Kalman filters,…

  • From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers

    From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science. Dr. Theophano Mitsa Go…

  • Supervised Dynamic Dimension Reduction with Deep Neural Network

    Supervised Dynamic Dimension Reduction with Deep Neural Network arXiv:2508.03546v1 Announce Type: new Abstract: This paper studies the problem of dimension reduction, tailored to improving time series forecasting with high-dimensional predictors. We propose a novel Supervised Deep Dynamic Principal component analysis (SDDP) framework that incorporates the target variable and lagged observations into the factor extraction process.…

  • Physics-informed machine learning: A mathematical framework with applications to time series forecasting

    Physics-informed machine learning: A mathematical framework with applications to time series forecasting arXiv:2507.08906v1 Announce Type: new Abstract: Physics-informed machine learning (PIML) is an emerging framework that integrates physical knowledge into machine learning models. This physical prior often takes the form of a partial differential equation (PDE) system that the regression function must satisfy. In the…

  • Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting

    Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting arXiv:2507.07469v1 Announce Type: new Abstract: Time-series models like ARIMA remain widely used for forecasting but limited to linear assumptions and high computational cost in large and complex datasets. We propose Galerkin-ARIMA that generalizes the AR component of ARIMA and replace it with a flexible…

  • Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics

    Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics arXiv:2506.20935v1 Announce Type: new Abstract: Forecasting geopolitical conflict from data sources like the Global Database of Events, Language, and Tone (GDELT) is a critical challenge for national security. The inherent sparsity, burstiness,…

  • Learning Data-Driven Uncertainty Set Partitions for Robust and Adaptive Energy Forecasting with Missing Data

    Learning Data-Driven Uncertainty Set Partitions for Robust and Adaptive Energy Forecasting with Missing Data arXiv:2503.20410v1 Announce Type: new Abstract: Short-term forecasting models typically assume the availability of input data (features) when they are deployed and in use. However, equipment failures, disruptions, cyberattacks, may lead to missing features when such models are used operationally, which could…

  • Mamba time series forecasting with uncertainty propagation

    Mamba time series forecasting with uncertainty propagation arXiv:2503.10873v1 Announce Type: new Abstract: State space models, such as Mamba, have recently garnered attention in time series forecasting due to their ability to capture sequence patterns. However, in electricity consumption benchmarks, Mamba forecasts exhibit a mean error of approximately 8%. Similarly, in traffic occupancy benchmarks, the mean…

  • Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature

    Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature arXiv:2503.06079v1 Announce Type: new Abstract: Despite the significance of probabilistic time-series forecasting models, their evaluation metrics often involve intractable integrations. The most widely used metric, the continuous ranked probability score (CRPS), is a strictly proper scoring function; however, its computation requires approximation. We found…

  • Forecasting time series with constraints

    Forecasting time series with constraints arXiv:2502.10485v1 Announce Type: new Abstract: Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for…

  • Triangle Forecasting: Why Traditional Impact Estimates Are Inflated (And How to Fix Them)

    Triangle Forecasting: Why Traditional Impact Estimates Are Inflated (And How to Fix Them) Accurate impact estimations can make or break your business case. Yet, despite its importance, most teams use oversimplified calculations that can lead to inflated projections. These shot-in-the-dark numbers not only destroy credibility with stakeholders but can also result in misallocation of resources and…

  • Influential Time-Series Forecasting Papers of 2023-2024: Part 1

    Influential Time-Series Forecasting Papers of 2023-2024: Part 1 This article explores some of the latest advancements in time-series forecasting. You can find the article here. Edit: If you know of any other interesting papers, please share them in the comments. submitted by /u/nkafr [link] [comments] /u/nkafr Go to original source

  • Demand Forecasting with Darts: A Tutorial

    Demand Forecasting with Darts: A Tutorial A hands-on tutorial with Python and Darts for demand forecasting, showcasing the power of TiDE and TFT Photo by Victoriano Izquierdo on Unsplash Demand forecasting for retailing companies can become a complex task, as several factors need to be considered from the start of the project to the final deployment. This…

  • Uncertainty Quantification in Time Series Forecasting

    Uncertainty Quantification in Time Series Forecasting A deep dive into EnbPI, a Conformal Prediction approach for time series forecasting Continue reading on Towards Data Science » Jonte Dancker Go to original source

  • Context-Aided Forecasting: Enhancing Forecasting with Textual Data

    Context-Aided Forecasting: Enhancing Forecasting with Textual Data A promising alternative approach to improve forecasting Continue reading on Towards Data Science » Nikos Kafritsas Go to original source