Latest Articles for Forecasting

A new method enhances forecasts by reconciling time series data in a hierarchical structure.

Combining traditional methods with neural networks improves data assimilation efficiency and accuracy.

A new approach improves forecast accuracy using flexible weighting.

The -Cell model merges traditional and machine learning techniques for predicting volatility.

Discover how machine learning improves short-term weather predictions.

A new method improves energy dispatch by blending forecasts with operational needs.

The CAP method enhances flu outbreak predictions for better public health responses.

SpecSTG improves traffic predictions by integrating spatial and temporal data.

Combining models to improve predictions of disease outbreaks.

A method to ensure accurate demand forecasts in dynamic service platforms.

A new model aims to improve forecasting across diverse data types.

A novel approach merging traditional models with machine learning improves infection predictions.

Learn how long memory influences predictions in various fields.

A new framework enhances solar energy predictions by utilizing various data sources.

A look at forecasting methods and their importance in economic decision-making.

A new MLP-based model improves accuracy in time series forecasting using random projection layers.

A new method uses human feedback to improve predictions in time series analysis.

This study examines if language models can match human forecasting abilities.

A tool for improving forecasting accuracy using counterfactuals and data visualization.

A new model enhances predictions for heavy rainfall events in Italy.

Examining how source data similarity and diversity impact forecasting accuracy.

A novel approach to understanding inflation through disaggregated data.

New methods improve predictions of future video frames using past data and depth.

A new method enhances predictions for wind and solar energy generation accuracy.

Learn how diffusion models enhance time series data analysis across various fields.

Analyzing prediction challenges during the mpox outbreak and their implications for future health crises.

A novel framework improving predictions using cognitive principles and precision weighting.

Aardvark Weather uses machine learning for accurate and efficient weather predictions.

A new framework enhances urban prediction models using limited data.

New methods boost disease modeling accuracy using historical peak data.

This research investigates using large language models for detecting anomalies in time series data.

TimeMixer combines detailed and broad data patterns for better forecasting accuracy.

TSER enhances forecasting accuracy by generating synthetic samples for underrepresented time series.

CATS model challenges traditional approaches in time series forecasting using cross-attention.

A new model enhances energy forecasts, reducing costs in electricity markets.

TSRMs offer a new way to analyze time series data effectively.

A method for collaborative machine learning while keeping data private.

This study assesses the forecasting abilities of language models compared to humans.

Renewable energy forecasting needs better coordination for efficiency and cost-effectiveness.

Examining how social trends shape energy consumption in the UK and Ireland.