Short-term fluctuations in dissolved oxygen are difficult to capture in warm outdoor Nile tilapia (Oreochromis niloticus) ponds using periodic manual measurements, yet they strongly influence fish performance and farm management. This study presents...
Precision livestock farming requires monitoring approaches that extend beyond static activity thresholds to enable dynamic, animal-level decision support. A time-aware machine learning framework was developed to transform RFID-derived goat activity r...
Excess indoor moisture can affect occupant health, damage building materials, and create conditions favorable to mold growth. Many monitoring systems rely on fixed thresholds and current sensor readings, offering little anticipation of unfavorable co...
Short-term electricity demand forecasting is essential for power grid stability and generation scheduling. Although forecasting models are commonly evaluated using one-step-ahead predictions, high accuracy at a short horizon does not necessarily impl...
Short-term facial landmark forecasting is important for anticipatory facial behavior in human–robot interaction, yet models trained with pointwise reconstruction losses often suffer from mean reversion, producing low-error predictions with weak...
The combustion temperature field is a key indicator of combustion stability, equipment integrity, and abnormal operating conditions. This study addresses short-horizon forecasting of laboratory-scale apparent infrared temperature fields using a Convo...
Accurate short-term load forecasting is essential for power system operations, including unit commitment, economic dispatch, and reserve scheduling. Regional load series exhibit temporal variation and horizon-dependent dynamics that make multi-horizo...
Accurate short-horizon forecasting of ship CO2 emissions is important for maritime carbon monitoring, operational assessment, and the transition toward sustainable maritime transportation. However, reliable forecasting remains challenging because shi...
Many real-world forecasting tasks involve heterogeneous temporal sources, where high-frequency dynamic observations coexist with low-frequency semantic or contextual information. Existing methods often treat these inputs through simple broadcasting o...
This paper examines whether a residual structural state extracted from cross-asset downside-risk dependence contains incremental information for forecasting next-day market downside risk beyond a strong heterogeneous autoregressive (HAR) benchmark. T...
Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considere...
This paper studies short-horizon trajectory forecasting through a coordinate-conditioned multiscale architecture, termed AeroMixer. The framework combines segment-wise local Cartesian re-representation, multiscale decomposition, bidirectional trend m...
Accurate time series forecasting of sea surface temperature (SST) is essential for understanding the ocean climate system and large-scale ocean circulation, yet it remains challenging due to regime-dependent variability and correlated errors across h...
Severe transients in nuclear power plants (NPPs) are strongly coupled and highly nonstationary, which makes reliable short-horizon multivariate forecasting difficult for conventional sequence models. To address this challenge, this study develops a h...
Proton exchange membrane fuel cell (PEMFC) durability under dynamic operation remains a major challenge because the observed voltage decay may combine persistent and transient performance changes. This study presents a condition-aware and data-driven...
Precision battery-health monitoring and rapid degradation detection are essential for improving the security, durability, and efficacy of electric vehicles (EVs). By incorporating short-term State-of-Health (SoH) forecasting, mid-term deterioration a...
Reliable management of lithium-ion battery thermal runaway requires knowing not only when abnormal heating begins, but also how far future temperature evolution can be predicted with useful accuracy. This study evaluates that question using 67 abuse...
The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets...
This work proposes a cascade model incorporating Long–Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which offers a more reliable model to forecast short-term (hourly) and medium horizon (week) water demand. The MLP model integrates...
Short-term GPU utilization forecasts are useful for scheduling, resource allocation, and capacity planning, but production traces are rarely smooth. They contain spikes, regime changes, idle periods, and incomplete observations. We study this problem...
The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecast...
Local power changes, weak-output states, and changing interval coverage complicate ultra-short-term wind power forecasting. This study develops power-increment enhancement and signed adaptive conformal inference (PIE-SACI) to address these challenges...
The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the powe...
Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, whe...
Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this vari...
Accurate hub-height wind-speed forecasting is a prerequisite for the safe and economical integration of wind power into the electricity grid, because forecasting errors propagate directly into imbalance penalties, reserve sizing, and turbine wear, an...
Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at...
Reliable short-term hydropower forecasting is essential for dispatch planning and early electricity procurement in snowmelt-influenced power systems. This study develops a leak-free operational forecasting framework using quality-controlled hourly ge...
Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these do...
The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable...
Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertaint...
This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model,...
Remote fault-diagnosis services in dynamic edge–cloud environments depend on timely monitoring-data upload, remote inference, and result delivery, making their communication layer sensitive to variations in available bandwidth, link latency, an...
Exchange-rate forecasting requires modelling non-stationary dynamics, heavy-tailed shocks, and complex temporal dependencies. However, forecasting performance in emerging-market currencies is fundamentally constrained by intrinsic dynamical instabili...
Accurate short-term forecasting of urban electricity demand is essential for operational planning and climate-resilient energy management. This study evaluates four forecasting models, namely, Prophet, Long Short-Term Memory (LSTM), Gated Recurrent U...
Time series forecasting is critical for decision-making in numerous domains, yet achieving high accuracy across both short-term and long-term horizons remains challenging. In this paper, we propose a general hybrid forecasting framework that integrat...
The increasing penetration of photovoltaic (PV) systems requires accurate and stable short-term forecasting to ensure reliable grid operation under operational constraints. This paper investigates short-horizon multi-step PV power forecasting using o...
The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for...
Multi-horizon forecasting of nonlinear dynamic time series with exogenous inputs is challenging when the target variable exhibits strong temporal persistence and the exogenous variables provide horizon-dependent corrective information. Direct forecas...
Large Language Models (LLMs) offer opportunities for processing heterogeneous information streams relevant to energy-market decision-making, but their practical role in forecasting-oriented analytical workflows remains uncertain. This paper presents...
Forecasting scrap steel prices is crucial for the economic sustainability of recycling operations, yet it remains challenging due to inherent volatility and non-stationary behavior. In this study, we develop and evaluate a delta-targeted Hybrid forec...
This paper presents a 30-day indoor ice rink dataset comprising 1,305,260 observations from twenty signals, a full 43,200-min calendar with acquisition gaps, and a 42,929-row reference preprocessed table produced by deterministic last-observation-car...
This study examines whether higher temporal resolution and more flexible model specification improve short-horizon forecasting of aggregate U.S. listed-real-estate exchange-traded fund returns. Using release-aware daily, monthly, and quarterly predic...
To improve short-horizon exchange rate forecasts, we employ foreign exchange market risk factors as fundamentals, and Bayesian treed Gaussian process (BTGP) models to handle non-linear, time-varying relationships between these fundamentals and exchan...
Geopolitical uncertainty may affect financial markets, but its incremental value for forecasting emerging-market stock returns remains unclear. Using monthly data from January 1990 to July 2026, this study compares ARIMA-GARCH and ARIMAX-GARCH benchm...
This study uses data from Taichung Fengyuan Station in Taiwan’s Civil IoT to conduct short-term forecasting of the Air Quality Index (AQI). We compile multiple pollutant and meteorological features and develop three models—Long Short-Term...
Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long fi...
Accurate short-term agricultural commodity price forecasting is important for market monitoring, procurement planning, inventory management, and price–risk awareness. This study develops a data-driven forecasting framework, namely CMACS–A...
New products have little sales history, which makes short-horizon demand forecasting difficult. This study tests whether a product–store–time graph can provide pre-origin relational context and whether task-conditioned meta-learning can a...
Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, w...
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