This paper proposes a novel non-parametric approach for the analysis and prediction of electricity price curves by applying the manifold learning methodology. Cluster analysis based on the embedded low-dimensional manifold of the original price data is employed to identify characteristics of the price curve shape. The proposed price curve model performs well in forecasting both short-term price...

quantitative methods used to forecast the behaviour of financial markets often produce unsatisfactory if not dismal results given the complex interactions between a given market's behaviour and other economic phenomena. Part of the problem lies in the fact that the relationships existing between financial markets and the economy as a whole are often poorly understood. On top of this there are a...

In this paper we consider the forecasting performance of a range of semiand non-parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and timeand price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financi...

Electricity price forecasting is an essential task in all the deregulated markets of world. The accurate prediction day-ahead electricity prices active research field and available data from various can be used as input for forecasting. A collection models have been proposed this task, but fundamental question on how to use big often neglected. In paper, we propose transfer learning a tool util...

Electricity markets are considered to be the most volatile amongst commodity markets. The non-storability of electricity and need for instantaneous balancing demand supply can often cause extreme short-lived fluctuations in prices. These termed price spikes. In this paper, we employ a multiclass Support Vector Machine (SVM) model forecast occurrence spikes German intraday market. As spikes, def...

This paper provides an effective approach for forecasting return volatility via threshold heteroskedastic models of the daily asset price range, defined as the difference between the highest and lowest log asset price recorded throughout the day. We propose a general model specification, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous varia...