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Thumbnail of dataset Forecasting the oil, gold, equity, and Bitcoin markets using robust multivariate range-based GARCH models
DatasetForecasting the oil, gold, equity, and Bitcoin markets using robust multivariate range-based GARCH models
Aug 16, 2026

Fiszeder, Piotr; Pietrzyk, Radosław, 2025, "Forecasting the oil, gold, equity, and Bitcoin markets using robust multivariate range-based GARCH models", https://doi.org/10.18150/AN5NET, RepOD, V2

Thumbnail of dataset News sentiment analysis using ChatGPT for Bitcoin price dynamics: Application of statistical and machine learning methods
DatasetNews sentiment analysis using ChatGPT for Bitcoin price dynamics: Application of statistical and machine learning methods
Aug 16, 2026

Fiszeder, Piotr; Pietrzyk, Radosław, 2025, "News sentiment analysis using ChatGPT for Bitcoin price dynamics: Application of statistical and machine learning methods", https://doi.org/10.18150/WEATTF, RepOD, V2

Thumbnail of dataset Multi-Source Information Fusion for Market Volatility: An Interpretable Statistical and Machine Learning Framework for the VIX Index
DatasetMulti-Source Information Fusion for Market Volatility: An Interpretable Statistical and Machine Learning Framework for the VIX Index
Apr 27, 2026

Fiszeder, Piotr; Pietrzyk, Radosław, 2026, "Multi-Source Information Fusion for Market Volatility: An Interpretable Statistical and Machine Learning Framework for the VIX Index", https://doi.org/10.18150/LF0FUW, RepOD, V1

Thumbnail of dataset Raw data from a research project "Towards more sustainable energy consumption in households in the EU energy peripheries"
DatasetRaw data from a research project "Towards more sustainable energy consumption in households in the EU energy peripheries"
Apr 13, 2026

Kola-Bezka, Maria, 2026, "Raw data from a research project "Towards more sustainable energy consumption in households in the EU energy peripheries"", https://doi.org/10.18150/KXEUWL, RepOD, V1

Thumbnail of dataset Identification of Bitcoin Volatility Drivers Using Statistical and Machine Learning Methods
DatasetIdentification of Bitcoin Volatility Drivers Using Statistical and Machine Learning Methods
Mar 2, 2026

Fiszeder, Piotr; Pietrzyk, Radosław, 2024, "Identification of Bitcoin Volatility Drivers Using Statistical and Machine Learning Methods", https://doi.org/10.18150/SJHAHR, RepOD, V2

Thumbnail of dataset Raw data from the research project "Energy transition of households in underdeveloped regions (on the example of Poland)", acronym: ETHUR
DatasetRaw data from the research project "Energy transition of households in underdeveloped regions (on the example of Poland)", acronym: ETHUR
Sep 5, 2025

Kola-Bezka, Maria, 2023, "Raw data from the research project "Energy transition of households in underdeveloped regions (on the example of Poland)", acronym: ETHUR", https://doi.org/10.18150/BWAVZU, RepOD, V2

Thumbnail of dataset Kalkulator Podatkowy 2023
DatasetKalkulator Podatkowy 2023
May 8, 2025

Płachta, Łukasz, 2025, "Kalkulator Podatkowy 2023", https://doi.org/10.18150/NO91AD, RepOD, V1

Thumbnail of dataset Analysis of Global Stock Market Development - Integration of Clustering, Classification, and Shapley Values
DatasetAnalysis of Global Stock Market Development - Integration of Clustering, Classification, and Shapley Values
Apr 28, 2025

Stawarz, Marcin, 2024, "Analysis of Global Stock Market Development - Integration of Clustering, Classification, and Shapley Values", https://doi.org/10.18150/OELMLK, RepOD, V2

Thumbnail of dataset Green FinTech: A Consumer Awareness Study
DatasetGreen FinTech: A Consumer Awareness Study
Apr 18, 2025

Piotrowska, Anna Iwona; Piotrowski, Dariusz, 2025, "Green FinTech: A Consumer Awareness Study", https://doi.org/10.18150/CSYNF7, RepOD, V1

Thumbnail of dataset Determining Multi-Class Trading Signals for Bitcoin: A Comparative Study of XGBoost, LightGBM, and Random Forest
DatasetDetermining Multi-Class Trading Signals for Bitcoin: A Comparative Study of XGBoost, LightGBM, and Random Forest
Jan 7, 2025

Stawarz, Marcin, 2025, "Determining Multi-Class Trading Signals for Bitcoin: A Comparative Study of XGBoost, LightGBM, and Random Forest", https://doi.org/10.18150/FXSBZP, RepOD, V1

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