With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-series stationary criteria to prepare data for further times-series related analysis is investigated. Three main analysis are conducted in this paper, including moving average, moving standard deviation and augmented Dickey-Fuller test. The data used in this paper is New England big data from 9 different operational zones. For each zone, 4 different variables including day-ahead (DA) electricity demand, price and real-time (RT) electricity demand price are considered.

-- 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.