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Time-series prediction
Predicting energy prices using: consumption and production of energy, total export/import, and the exogenous factor – average daily temperature (capital city temperature as a proxy), as predictors.
Many of the proposed methods yielded poor results due to the high level of multi-collinearity in the data set. The metrics derived from shrunk regression models did not show significant variation compared to the metrics from ordinary regression models. Log-transformation and differentiation guaranteed stationary in the time-series data sets.
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