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 behavior or progression of series), cyclic component (non-periodic...
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| creator | Rakhshandehroo, Mohsen Rajabdorri, Mohammad |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.1907.13016 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.1907.13016</identifier><language>eng</language><subject>Statistics - Machine Learning</subject><creationdate>2019-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1907.13016$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1907.13016$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Rakhshandehroo, Mohsen</creatorcontrib><creatorcontrib>Rajabdorri, Mohammad</creatorcontrib><title>Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis</title><description>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.</description><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNo9kM1OhDAUhdm4MKMP4Mr7AmA7UErdITOjJpNoInvSn1vTCJSUZiJvLzMaV9_m5Jx8J0nuKMmKijHyIMO3O2VUEJ7RnNDyOjm1bkD4wOBwhnqU_TK7GbyFJ_cJOxklWB9g36OOwWkXF3hfiSBHAzsczogeDm5lsygMaR2j1F8zTDJE2D6uIe2Hyc8uOj_-L9wkV1b2M97-cZO0h33bvKTHt-fXpj6msuRlKgSzWiljWamrQmytkUogJ6akBRXICoOKVFsuGdJVLy80NQS5MUpw5LbKN8n9b-1FvJuCG2RYuvMB3eWA_Acnllat</recordid><startdate>20190730</startdate><enddate>20190730</enddate><creator>Rakhshandehroo, Mohsen</creator><creator>Rajabdorri, Mohammad</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20190730</creationdate><title>Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis</title><author>Rakhshandehroo, Mohsen ; Rajabdorri, Mohammad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-995fcbbdf56c8492fdab9e70d61419e54deb0827a5e155034c1d0e7ddb97e7f83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Rakhshandehroo, Mohsen</creatorcontrib><creatorcontrib>Rajabdorri, Mohammad</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rakhshandehroo, Mohsen</au><au>Rajabdorri, Mohammad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis</atitle><date>2019-07-30</date><risdate>2019</risdate><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.</abstract><doi>10.48550/arxiv.1907.13016</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Machine Learning |
| title | Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis |
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