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Charalampos M. Liapis
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2020 – today
- 2024
- [j7]Charalampos M. Liapis
, Aikaterini Karanikola
, Sotiris Kotsiantis:
Data-efficient software defect prediction: A comparative analysis of active learning-enhanced models and voting ensembles. Inf. Sci. 676: 120786 (2024) - 2023
- [j6]Charalampos M. Liapis
, Aikaterini Karanikola, Sotiris Kotsiantis
:
Investigating Deep Stock Market Forecasting with Sentiment Analysis. Entropy 25(2): 219 (2023) - [j5]Aikaterini Karanikola
, Gregory Davrazos
, Charalampos M. Liapis
, Sotiris Kotsiantis
:
Financial sentiment analysis: Classic methods vs. deep learning models. Intell. Decis. Technol. 17(4): 893-915 (2023) - [j4]Charalampos M. Liapis
, Sotiris Kotsiantis
:
Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting. Inf. 14(11): 596 (2023) - [j3]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
A multivariate ensemble learning method for medium-term energy forecasting. Neural Comput. Appl. 35(29): 21479-21497 (2023) - 2022
- [c4]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
Energy Load Forecasting: Investigating Mid-Term Predictions with Ensemble Learners. AIAI (1) 2022: 343-355 - [c3]Charalampos M. Liapis, Sotiris Kotsiantis:
Energy Balance Forecasting: An Extensive Multivariate Regression Models Comparison. SETN 2022: 41:1-41:7 - 2021
- [j2]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis
:
A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting. Entropy 23(12): 1603 (2021) - [j1]Aikaterini Karanikola, Charalampos M. Liapis, Sotiris Kotsiantis:
Investigating cluster validation metrics for optimal number of clusters determination. Intell. Decis. Technol. 15(4): 809-824 (2021) - [c2]Aikaterini Karanikola, Charalampos M. Liapis, Sotiris Kotsiantis:
A comparative study of validity indices on estimating the optimal number of clusters. IISA 2021: 1-8 - 2020
- [c1]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
An ensemble forecasting method using univariate time series COVID-19 data. PCI 2020: 50-52
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