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Breaking New Ground with EPOCH: AI and Web Intelligence Transform Price Forecasting

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.

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Research and Innovation spin-off of
The Story
• Commodity Prices can be influenced by the media
 Commodities: oil, wood, grains, gold, etc.
 Local media (e.g., Der Standard, ORF, etc)
 Social Media (e.g., Twitter, Facebook)
 Sentiment can be used as a predictor for price changes
• Research Questions
 Does this happen on a local scale too? (e.g., country-level)
 What kind of features are good predictors?
Datasets
• Different time series – extracted from larger dataset
 Wood related products in Austria, compiled by KPMG
 2017-2021 – but portal data only from January 1 ,2019
 Ended up with January 1 , 2020 – May 30, 2021
 Target: price change prediction (percentage)
• Features
 Aggregated Sentiment and Frequency Counts
Feature Extractor
Features
Baselines (MAE)

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Breaking New Ground with EPOCH: AI and Web Intelligence Transform Price Forecasting

  • 2. The Story • Commodity Prices can be influenced by the media  Commodities: oil, wood, grains, gold, etc.  Local media (e.g., Der Standard, ORF, etc)  Social Media (e.g., Twitter, Facebook)  Sentiment can be used as a predictor for price changes • Research Questions  Does this happen on a local scale too? (e.g., country-level)  What kind of features are good predictors?
  • 3. Datasets • Different time series – extracted from larger dataset  Wood related products in Austria, compiled by KPMG  2017-2021 – but portal data only from January 1 ,2019  Ended up with January 1 , 2020 – May 30, 2021  Target: price change prediction (percentage) • Features  Aggregated Sentiment and Frequency Counts
  • 8. Insights? • What about explainability?  Which models are better?  Which features matter? • Tools  mlflow – evaluation  shap – explainability  streamlit – analytics - interfaces
  • 10. However • SHAP is slow – 4 data points / minute  Scalability is an issue  The number of features we can compare is rather low • Best Model: GRU • Top features  Rolling-Wetter-MeanSentiment  Rolling-Schadling-MeanSentiment, Schadling-TotalFrequency
  • 11. Thank you for your attention!