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Update with changes from development
Final commit containing: -PositionalEncoding module for the hybrid model -custom Bandwidth dataset for uniform handling of the data for the hybrid model - the hybrid model -the training and prediction methods for the hybrid model -helper spectral loss method -extract method, mainly based FTIO's core - train arima method, which is essenially the main entrypoint for training and forecasting using the ARIMA and SARIMA models
Small bug fix in machine_learning models added test cases added documentation and examples in the form of mark down document applied code style
Refactored files into directories
changed broken import to proper directory
removed trailing widespaces for style
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Bachelor Thesis: A Hybrid Approach to Improve Frequency-Based I/O Analysis with Machine Learning
Implemented machine-learning models to further explore analysis of I/O behavior: hybrid-model (Transformer + LSTM), ARIMA and SARIMA.