WaveHiTS: Wavelet-Enhanced Hierarchical Model for Wind Direction Nowcasting in Inner Mongolia

A revised arXiv paper introduces WaveHiTS, a wavelet-enhanced hierarchical time series model designed for short-term wind direction forecasting in eastern Inner Mongolia. The approach targets common difficulties in directional data, including circular values, multi-step error accumulation, and complex meteorological interactions. It was cross-listed on arXiv's cs.LG and cs.AI categories.

PPDL framework combines physical priors with deep learning for user retention forecasting

Researchers present PPDL, a framework for predicting channel-level user retention ratios in multi-channel paid user acquisition. The method integrates physical priors with deep learning to capture the sharp early churn typical of retention curves. Accurate early forecasts are intended to help marketers allocate advertising budgets more effectively.

arXiv Paper Proposes Horizon-Specific Expert Fusion for Solar Power Forecasting

A new arXiv preprint describes a hierarchical ensemble method for short-term photovoltaic power forecasting. The approach pairs temporal neural models with expert fusion that adapts to the forecast horizon, reflecting that regular solar cycles and weather-driven variation matter differently at different lead times. The work targets improved accuracy in solar generation forecasts.

Hybrid Machine-Learning Model Adds Uncertainty Estimates to Irrigation Scheduling

A new arXiv paper presents a hybrid mathematical and machine-learning approach for irrigation decision support that also quantifies confidence in its soil-moisture forecasts. The authors note that irrigation is typically scheduled reactively, even though agriculture uses about 70% of global freshwater withdrawals. The model aims to give growers both a forward-looking moisture prediction and a measure of how reliable that prediction is.

Hybrid TCN-Transformer Model Predicts Satellite Collision Risk from Conjunction Data

Researchers present a hybrid temporal convolutional network and transformer architecture designed to forecast satellite collision probability early, using conjunction data messages as input within a dedicated analysis framework. The work targets the growing operational load on satellite operators caused by more frequent close approaches in low Earth orbit as satellites and debris multiply.

Study Tests Whether SNOWPACK Simulations Can Predict Satellite-Mapped Avalanches

Researchers examined whether simulated snowpack conditions from the SNOWPACK model can serve as a data-driven predictor of avalanche activity detected by satellite imagery. The work targets regions where direct field observations are too sparse to support conventional forecasting. It points toward filling observational gaps in mountain avalanche monitoring with model-based estimates.

GNN4PPM: Graph Neural Networks for Multi-Target Predictive Process Monitoring

A new arXiv paper proposes GNN4PPM, which applies relational graph convolutional networks to predictive process monitoring. The method targets several predictions at once, such as the next event in a running process, the time remaining until a trace finishes, and its eventual outcome. The authors argue that existing techniques typically address only one of these targets; the posted abstract is truncated before any experimental results are described.

arXiv Paper Proposes Human-Supervised Architecture for Pharma Sales Forecasting

A new arXiv preprint introduces Phorecaster365, a reference architecture that combines automated forecasting with human oversight for pharmaceutical sales and planning decisions. The work argues that forecast interpretation depends on context such as inventory levels, transaction semantics, product lifecycle stage, and the information available at the time each forecast was made. It is positioned as a decision-support design rather than a deployed commercial system.

Calibeating generalized from quadratic scoring to all proper scoring rules

A new arXiv paper extends the concepts of calibrated forecasts and calibeating, which were previously defined only for the standard quadratic scoring rule, to the full class of proper scoring rules. The authors develop these notions in this broader setting, where truthful reporting is a defining property of the rule. The work aims to show how calibration-based guarantees carry over beyond the squared-error case.

Hypergraph-Enhanced Mixture-of-Experts Model Targets Urban Traffic Forecasting

A new arXiv paper introduces STHMoE, a mixture-of-experts architecture that uses hypergraphs to coordinate heterogeneous dependencies in spatio-temporal traffic data. The method is designed to handle the non-stationary and structurally dynamic patterns produced by large networks of urban sensors. It targets LLM-based forecasting for intelligent transportation systems.

Study Examines When Ensemble Models Help Photovoltaic Forecasting

A new arXiv paper analyzes the trade-offs of using ensembles for solar power forecasting, noting that added components can raise computation without improving accuracy. The authors ran matched comparisons and ablation tests on a fixed set of diverse predictors, using hourly data to isolate each component's contribution. The work aims to clarify when ensemble complexity is justified for photovoltaic prediction.

Benchmark Ties Intermittent-Demand Forecasting to Order-Level Service Metrics

A new arXiv paper argues that forecasting models for spare-parts logistics are typically chosen by line-level accuracy, even though operators are paid on order-level service, where an order only counts if every requested line ships. The authors present a decision-aware benchmark aimed at closing that gap for intermittent demand. The work highlights how misaligned evaluation metrics can lead to poor operational choices.

Study Asks Whether Gradient Boosting Models Fit Intermittent Demand Forecasting

A new arXiv paper examines how well gradient boosting methods handle demand forecasting for products with intermittent, sporadic sales patterns. Such cases are difficult because standard forecasting approaches tend to struggle when demand is irregular. The work assesses whether gradient boosting is an appropriate tool in this setting.

WaVeFuse Model Combines Wavelet Denoising and Attention for Equity Index Forecasting

A new arXiv paper introduces WaVeFuse, a hybrid deep learning approach for forecasting stock market indices. The method targets three issues in existing models: noise from OHLCV data leaking into derived technical indicators, treating all channels the same during multi-scale decomposition, and mismatched frequency signals. It applies channel-wise wavelet denoising with vertical attention fusion to adapt across market regimes.

Diffusion Models Applied to Spatiotemporal Influenza Forecasting

A new arXiv paper explores using generative diffusion models to forecast influenza incidence across space and time. The authors argue that existing mechanistic and statistical methods often fail to capture complex epidemic dynamics, and propose a generative approach as an alternative. The work targets public health planning, where better short-term forecasts can inform preparedness decisions.

Wharton finance professor assesses whether AI's trillion-dollar investment will pay off

A finance professor at the University of Pennsylvania's Wharton School set out to estimate how AI will affect the economy in the coming years, working around a wide range of business and technical unknowns. She begins from a widely accepted economic fact as an anchor for the analysis. The work frames the central question of whether massive AI spending can deliver matching returns.

AWS Post Shows Automated Retail Replenishment Loop Using Databricks Genie and Amazon Quick

An AWS Machine Learning Blog tutorial describes how to combine a demand-forecasting model (MMF) with Databricks Genie and Amazon Quick to automate retail replenishment. The pipeline detects demand spikes, checks them against current supplier stock, and then places orders without human intervention. The post frames forecasting as largely solved and argues the real difficulty lies in acting on those predictions.

Study Combines Seismic Statistical Features and VQ-VAE for Earthquake Prediction

A new arXiv paper extends earlier work showing that 60 seismic statistical features outperform hundreds of generic features for earthquake prediction when used with XGBoost on Japanese and Chilean catalogue data. The authors add a vector-quantized variational autoencoder to the pipeline, aiming to improve how well seismicity can be predicted across space and time.

Google Research releases TimesFM-3, a 330M-parameter forecasting model

Google Research has introduced TimesFM-3, a model designed to forecast time series by combining historical data with related signals and known upcoming events such as promotions or weather. It generates all future time points at once rather than predicting one step at a time, and has 330 million parameters.

Predicting Electrical Outage Restoration Times with Longitudinal Tabular Transformers

Researchers have revised a study on predicting Estimated Times of Restoration, the timelines utilities publish for storm-related power outages. The work reframes ETR prediction as a longitudinal task, using tabular transformer models instead of the static approaches used in earlier research. Because restoration estimates shape customer decisions about food storage, medical devices, and evacuation, improving their accuracy has direct practical value.

KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning

Researchers introduce KairosAgent, an agentic approach to forecasting time series that span multiple domains and data types. The framework is designed to pair careful numerical analysis with semantic interpretation and to fuse information from different modalities. The paper is a revised version (v2) recently updated on arXiv.

SurF: A Generative Model for Multivariate Irregular Time Series Forecasting

Researchers introduce SurF, a generative model designed for multivariate event streams that are sampled at irregular intervals. The work argues that tokenization-based approaches struggle when the gaps between events span orders of magnitude, and proposes an alternative suited to such data. The paper is a revised arXiv submission in machine learning.

Study examines trade-off between forecast horizon length and learnability in autoregressive models

A new study investigates how far into the future autoregressive models should be trained when forecasting dynamical systems. The authors identify a trade-off between predictive performance and learnability as the training horizon grows, suggesting an optimal horizon exists. The findings offer practical guidance for selecting prediction horizons during model training.

LiFTER: A Neuro-Symbolic Method for Interpretable Continuous-Time Graph Forecasting

Researchers introduce LiFTER, a neuro-symbolic framework aimed at making continuous-time dynamic graph forecasting more transparent. Instead of leaving link predictions hidden inside opaque neural states that compress past interactions, the method surfaces which entities are shared across events and how temporal patterns contribute to forecasts. A revised version (v2) of the preprint has been posted on arXiv.

Machine Learning and Weather Data Used to Predict Train Delays in Finland

Researchers developed a machine learning approach that forecasts railway delays in Finland by combining weather observations with other operational data. The work situates such environmental sensing within future 6G-enabled wireless sensor networks and edge computing, which the authors argue will improve real-time reliability. The study is published as an arXiv preprint.

CryptoL Framework Targets Scale Imbalance in Cryptocurrency Forecasting

A new arXiv paper introduces CryptoL, a unified framework for forecasting multivariate cryptocurrency time series. The work addresses cross-asset scale differences, non-stationary market dynamics, and dependencies among open, high, low, and close price variables. It proposes physics-informed constraints to mitigate these issues in financial prediction.

arXiv paper integrates rainfall data into water-quality forecasting models

A revised arXiv preprint proposes a method for learning intrinsic water-quality dynamics that incorporates rainfall as an environmental driver. Rainfall affects water quality through runoff, pollutant transport, dilution and resuspension, processes that mechanistic models describe explicitly but which are hard to capture in purely data-driven approaches. The work aims to combine these perspectives for improved forecasting.

Context-Augmented LLMs Used to Improve Financial Forecasting with Alternative Data

A new arXiv paper examines how large language models can incorporate alternative data sources, such as consumer transactions, web traffic, and prediction markets, when forecasting a company's future financial performance. The authors argue these non-traditional signals offer timely insight into a firm's operating activity and propose augmenting LLMs with contextual information to make such data usable in forecasting tasks.

Google's WeatherNext AI outperforms standard methods in cyclone forecasting

A study published in Nature reports that Google's WeatherNext AI model forecasts cyclones more accurately than conventional systems. The model matches the precision that older methods only achieved a day later, effectively adding roughly 24 hours of advance warning. The approach could give communities more time to prepare for severe storms.

Anthropic economic model treats CEO's bleak job forecasts as extreme case

Anthropic has published an economic scenario model projecting three possible paths for the US economy through 2030. In the most extreme case, output would double every 4.5 years while unemployment among knowledge workers climbs to 17.9%. The company presents that outcome as a worst-case scenario rather than a likely baseline.

Google DeepMind releases WeatherNext 3 global weather forecasting model

Google DeepMind has announced WeatherNext 3, the newest version of its machine-learning system for global weather prediction. The company positions the update as a step up in forecast accuracy over earlier WeatherNext versions. The model extends DeepMind's weather AI line, which is used by businesses and organizations for planning and risk assessment.

WHY IT MATTERS ↘Iterative releases of operational ML forecasters like WeatherNext 3 show AI weather models moving from research demos to versioned commercial products, undercutting the cost and latency of supercomputer-based numerical prediction for energy, insurance, and logistics customers. It also tightens competitive pressure on public forecasting agencies and rivals such as NVIDIA to match accuracy at production scale.