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

TL;DR

This paper explores time series decomposition methods to analyze electricity demand and price data for detecting cyber-attacks, focusing on error pattern analysis after decomposition.

Contribution

It compares additive and multiplicative decomposition methods for time series analysis in cyber-attack detection within electricity systems.

Findings

Decomposition methods reveal patterns in data errors indicative of cyber-attacks.

Error term analysis suggests potential cyber-attack signals.

Multiplicative decomposition may better capture certain data behaviors.

Abstract

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 behavior or progression of series), cyclic component (non-periodic fluctuation behavior which are usually long term), seasonal component (periodic fluctuations due to seasonal variations like temperature, weather condition and etc.) and error term. For our case of cyber-attack detection, in this paper, two common ways of TS decomposition are investigated. The first method is additive decomposition and the second is multiplicative 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 cyber-attack to the system.