---
title: Hybrid Wind Forecast Model
url: https://www.ml-quant.com/papers/ssrn/4957172/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4957172
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4957172
featured: 2024-09-18
citations: unknown
topic: Econometrics & Forecasting
---


# Hybrid Wind Forecast Model

The study introduces a new hybrid ensemble model for accurate wind speed forecasting, utilizing signal decomposition, deep learning models, and metaheuristic optimization algorithms.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4957172
- Identifier: SSRN 4957172
- Released: 2024-05-25
- First featured: Quant Letter No. 66 (2024-09-18): https://www.ml-quant.com/issues/2024-09-18/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [Quantile deep learning models for multi-step ahead time series prediction](https://www.ml-quant.com/papers/arxiv/2411.15674/): The article introduces a new deep learning framework for predicting multi-step time series, which improves the performance of deep learning models. It has been effectively tested on Bitcoin and Ethereum, demonstrating its ability to manage volatility and provide useful information for decision-making.
- [Geometric Deep Learning for Realized Covariance Matrix Forecasting](https://www.ml-quant.com/papers/arxiv/2412.09517/): A new method for forecasting asset return covariance matrices using a Riemannian-geometry-aware deep learning framework outperforms traditional methods by considering the geometric properties of the matrices.
- [Distributional Refinement Network: Distributional Forecasting via Deep Learning](https://www.ml-quant.com/papers/arxiv/2406.00998/): The article introduces the Distributional Refinement Network (DRN), a model that enhances predictive performance and interpretability in actuarial modelling by merging a baseline model with a flexible neural network.
- [A Comparison of Standard Statistical, Machine Learning and Deep Learning Methods in Forecasting the Time Series](https://www.ml-quant.com/papers/ssrn/4840148/): The accuracy of Machine Learning and Deep Learning in forecasting macroeconomic indicators is compared to the traditional statistical method ARIMA.
- [A Dynamic Regime-Switching Model Using Gated Recurrent Straight-Through Units](https://www.ml-quant.com/papers/ssrn/4810879/): The Gated Recurrent Straightthrough Unit (GRSTU), a new deep learning model, outperforms statistical jump models in identifying regime changes in the S&P500 index, especially with smaller datasets.
- [Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues](https://www.ml-quant.com/papers/arxiv/2307.11888/): The study reveals that deep neural networks using linear complex-valued RNNs and MLPs can accurately approximate regular causal sequence-to-sequence maps, with complex eigenvalues near unit disk aiding in information storage.