Predicting stock market volatility using neural network models : a comparative study on Bidirectional Temporal Convolutional Network (BiTCN), Neural Hierarchical Interpolation for Time Series (NHITS), Time-series Dense Encoder (TiDE), and Temporal Fusion Transformer (TFT) across 20 years of S&P 500 constituent stocks

Mendoza Ortiz, J.A.

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Tilburg University

Predicting stock market volatility using neural network models : a comparative study on Bidirectional Temporal Convolutional Network (BiTCN), Neural Hierarchical Interpolation for Time Series (NHITS), Time-series Dense Encoder (TiDE), and Temporal Fusion Transformer (TFT) across 20 years of S&P 500 constituent stocks

dc:creator Mendoza Ortiz, J.A.

dc:date2025

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Author dc:creator: Mendoza Ortiz, J.A.
Contributors dc:contributor: Zamberlan, F.

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Statement dc:rights: (c) Universiteit van Tilburg
Language dc:language: eng

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Repository record dc:identifier: https://tilburguniversity.on.worldcat.org/search?queryString=scr.uvt.nl:11117469
OAI identifier oai:identifier: oai:scr.uvt.nl:11117469

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Harvested from: Tilburg University
Base URL: arno.uvt.nl/oai/scr.uvt.nl.cgi
Last updated: 2026-07-24
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Mendoza Ortiz, J.A.. Predicting stock market volatility using neural network models : a comparative study on Bidirectional Temporal Convolutional Network (BiTCN), Neural Hierarchical Interpolation for Time Series (NHITS), Time-series Dense Encoder (TiDE), and Temporal Fusion Transformer (TFT) across 20 years of S&P 500 constituent stocks. 2025. https://tilburguniversity.on.worldcat.org/search?queryString=scr.uvt.nl:11117469

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