Forecasting electricity prices in the Czech spot market
|Author:||Bc. Kryštof Černý|
|Year:||2016 - winter|
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||This master thesis is focused on analysis and forecasting of hourly and daily
electricity price on the deregulated Czech daily electricity market. The methods
used for estimating and forecasting hourly and daily prices are picked from the
ARIMA-GARCH family of models and Neural Networks. For daily price data,
the Redundant Haar Wavelet Transform decomposition of the time series is used
in combination with ARIMA and Neural Networks models for forecasting. For
hourly data, ARIMA and Neural Network models are considered.
The forecasting results of daily data indicate that simpler models such as
seasonal ARIMA outperform all other methods. Also the wavelet decomposition
of the daily series didn’t prove useful in enhancing the forecast precision.
For hourly data, the Multilayer Perceptron architecture of the neural network
outperformed the ARIMA forecast.