Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis

-- In this paper, in following of the first part (which ADF tests using ACI evaluation) has conducted, Time Series (TSs) are analyzed using decomposition analysis. In fact, TSs are composed of four components including trend (long term be - haviour or progression of series), cyclic component ( non - periodic fluctuation behaviour which are usually long term), seasonal component (periodic fluctuations due to seasonal variations like temperature, weather condition and etc.) and error term. The first method is additive decomposition and the second is mu ltiplicative method to decompose a TS into its components. After decomposition, the error term is tested using Durbin - Watson and Breusch - Godfrey test to see whether the error follows any predictable pattern, it can be concluded that there is a chance of cy ber - attack to the system. In this paper, to find out that TS errors (or called residual's interchangeably)follows any particular patterns or not and to obtain t he residual values of TSs, we conducted two classical methods of TS decomposition and then we analyzed the residual terms of TSs for both decomposition method to find anomaly in residual distributions.