The FFG funded project EPOCH, coordinated by MODUL Technology, demonstrated the groundbreaking use of machine learning/AI approaches to time series forecasting combined with Web intelligence – the analysis of topics and trends in online news and social media over time. Supported by webLyzard technology’s Web intelligence platform and wood price data from KPMG, MODUL Technology’s AI experts combined statistical forecasting models with features extracted from the online news and social media based on mentions of terms relevant to the wood market and combined sentiment of documents mentioning the terms (e.g. ‘Borkenkäfer’, the bark beetle which damages trees).  Four categories of terms were created, and were tested for their predictive power (the extent to which they improve the accuracy of price prediction compared to pure statistical forecasting). We compared a number of models (Random Forest, XGBoost, LSTM, GRU) with four different datasets (hardwood, softwood, nadelholz (coniferous), rohpapier (raw paper)). The rolling mean sentiment feature was found to be the most predictive for future wood prices, especially for the terms related to weather and pests. We provide a slideset explaining the wood price prediction experiments and a Web interface which allows you to visualise the accuracy and feature importance in prediction using different models and datasets. The experiment code is also available to test the models with your own price data. 

• Slides
• Web interface
• Experiment code

This groundbreaking work was extended to look at predictions of prices in other markets and make use of recent Transformers models such as FinBERT to generate the sentiment features from a news corpus. The developed models outperformed classical models, showcasing the fact that combining basic economic logic (e.g., supply and demand) with affective and background knowledge can lead to significant improvements. MODUL Technology will continue to bring this advance in price prediction to other domains through future projects. 

Journal Articles

• Kaplan, H., Weichselbraun, A., Braşoveanu, A.M.P. (2023) Integrating Economic Theory, Domain Knowledge and Social Knowledge into Hybrid Sentiment Models for Predicting the Crude Oil Markets. Cognitive Computation. Springer Nature. DOI: 10.1007/s12559-023-10129-4.
• Weichselbraun, A., Steixner, J., Brasoveanu, A.M.P., Scharl, A., Gobel, M & Nixon, L.B. (2022). Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications. Cognitive Computation 14(1), pp. 228-245. DOI: 10.1007/s12559-021-09839-4.

Book Chapters

• Braşoveanu, A M.P., & Andonie, R. (2022). Visualizing and Explaining Language Models. In Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 213-237. Cham: Springer International Publishing. DOI: 10.1007/978-3-030-93119-3_8.