Energy Time Series Data Analysis based on a Novel Integrated Data Characteristic Testing Approach

This paper attempts to propose an integrated data characteristic testing approach for energy time series data so as to analyze the energy dynamics, which serves as the foundation for the model selection problem. Based on thoroughly analyzing the main data characteristics of energy time series data together with their interrelationship, these data characteristics are divided into two main categories: nature and pattern characteristics to explore energy time series data from different perspectives. In nature determination, the energy time series data is analyzed in terms of nonstationarity, nonlinearity and complexity characteristics from a global perspective. In pattern measurements, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) signify the relative hidden patterns and the impacts on the original data, via a way of decomposition. For illustration purpose, hydropower consumptions in China and USA are analyzed and the main data characteristics are thoroughly explored by using the proposed integrated approach. Empirical results reveal that besides same characteristics of difference strationarity, nonlinearity and seasonality, the hydropower markets in China and USA are quite different: while China's hydropower market are comparatively simple but sensitive to emergencies, e.g., government support and technological progress, US’ hydropower market is otherwise mature and efficient with the nature of high leveled complexity and the main pattern of randomicity. The results also confirm the proposed integrated approach an effective tool to test energy time series data in terms of data characteristics, paving the way for the further model formulation and forecasting.