title-bg-circle

Consulting
services

title-bg-circle

Consulting
services

Time-series prediction

Problem

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.

Solution

  1. Development of statistical models (single/multivariable regression with OLS, robust OLS, ridge and lasso regression) that eliminate (multi) collinearity in the data set.
  2. Development of (S)ARIMA(X) models that improve performance over models developed in (i).

Results

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.