Seasonality - Revision history

99.99.242.16 at 16:48, 6 September 2024

2024-09-06T16:48:24Z

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	Organisations facing seasonal variations, such as ice-cream vendors, are often interested in knowing their performance relative to the normal seasonal variation. Seasonal variations in the labour market can be attributed to the entrance of school leavers into the job market as they aim to contribute to the workforce upon the completion of their schooling. These regular changes are of less interest to those who study employment data than the variations that occur due to the underlying state of the economy; their focus is on how unemployment in the workforce has changed, despite the impact of the regular seasonal variations.<ref name="otexts.org"/>		Organisations facing seasonal variations, such as ice-cream vendors, are often interested in knowing their performance relative to the normal seasonal variation. Seasonal variations in the labour market can be attributed to the entrance of school leavers into the job market as they aim to contribute to the workforce upon the completion of their schooling. These regular changes are of less interest to those who study employment data than the variations that occur due to the underlying state of the economy; their focus is on how unemployment in the workforce has changed, despite the impact of the regular seasonal variations.<ref name="otexts.org"/>

	It is necessary for organisations to identify and measure seasonal variations within their market to help them plan for the future. This can prepare them for the temporary increases or decreases in labour requirements and inventory as demand for their product or service fluctuates over certain periods. This may require training, periodic maintenance, and so forth that can be organized in advance. Apart from these considerations, the organisations need to know if variation they have experienced has been more or less than the expected amount, beyond what the usual seasonal variations account for.		It is necessary for organisations to identify and measure seasonal variations within their market to help them plan for the future. This can prepare them for the temporary increases or decreases in labour requirements and inventory as demand for their product or service fluctuates over certain periods. This may require training, periodic maintenance, and so forth that can be organized in advance. Apart from these considerations, the organisations need to know if variation they have experienced has been more or less than the expected amount, beyond what the usual seasonal variations account for.<ref>{{Cite web \|title=2 Tips to Maximize Profits in Business \|url=https://www.netsuite.com/portal/resource/articles/business-strategy/maximize-profitability.shtml \|website=netsuite}}</ref>

	==Motivation==		==Motivation==

24.206.115.31 at 06:50, 26 June 2024

2024-06-26T06:50:14Z

← Previous revision		Revision as of 06:50, 26 June 2024
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	{{More citations needed\|date=November 2010}}		{{More citations needed\|date=November 2010}}
	}}		}}
	In [[time series]] data, '''seasonality''' is the that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html\|title=Archived copy\|access-date=2015-05-13\|archive-date=2015-05-18\|archive-url=https://web.archive.org/web/20150518104248/http://www.businessdictionary.com/definition/seasonality.html\|url-status=dead}}</ref> of a time series.		In [[time series]] data, '''seasonality''' refers to the trends that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html\|title=Archived copy\|access-date=2015-05-13\|archive-date=2015-05-18\|archive-url=https://web.archive.org/web/20150518104248/http://www.businessdictionary.com/definition/seasonality.html\|url-status=dead}}</ref> of a time series.

	Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>		Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>

Sammi Brie: Adding local short description: "Variations in data at specific regular intervals less than a year", overriding Wikidata description "presence of variations that occur at specific regular intervals less than a year"

2024-06-13T04:49:46Z

Adding local short description: "Variations in data at specific regular intervals less than a year", overriding Wikidata description "presence of variations that occur at specific regular intervals less than a year"

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			{{Short description\|Variations in data at specific regular intervals less than a year}}
	{{Multiple issues\|		{{Multiple issues\|
	{{More footnotes\|date=November 2008}}		{{More footnotes\|date=November 2008}}

2405:201:A41D:D9C9:7563:EECB:D9CE:51B6 at 18:22, 31 May 2024

2024-05-31T18:22:28Z

← Previous revision		Revision as of 18:22, 31 May 2024
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	{{More citations needed\|date=November 2010}}		{{More citations needed\|date=November 2010}}
	}}		}}
	In [[time series]] data, '''seasonality''' is the ~~presence of variations~~ that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html\|title=Archived copy\|access-date=2015-05-13\|archive-date=2015-05-18\|archive-url=https://web.archive.org/web/20150518104248/http://www.businessdictionary.com/definition/seasonality.html\|url-status=dead}}</ref> of a time series.		In [[time series]] data, '''seasonality''' is the that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html\|title=Archived copy\|access-date=2015-05-13\|archive-date=2015-05-18\|archive-url=https://web.archive.org/web/20150518104248/http://www.businessdictionary.com/definition/seasonality.html\|url-status=dead}}</ref> of a time series.

	Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>		Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>

Bender235: /* References */ removed general statistics books, added relevant time series books

2024-04-14T21:48:46Z

References: removed general statistics books, added relevant time series books

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 ==References==
 {{Reflist}}
 ==External links==

Bender235: /* See also */

2024-04-13T23:13:08Z

49.249.156.158: /* Ratio to trend method */

2023-06-15T06:52:11Z

Ratio to trend method

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	# Find the centered 12 monthly (or 4 quarterly) moving averages of the original data values in the [[time-series]].		# Find the centered 12 monthly (or 4 quarterly) moving averages of the original data values in the [[time-series]].
	#:		#:
	# Express each original data value of the time-series as a percentage of the corresponding centered [[moving average]] values obtained in step(1). In other words, in a multiplicative time-series model, we get (Original data values) / (Trend values) × 100 = ({{mvar\|T}} × {{mvar\|C}} × {{mvar\|S}} × {{mvar\|I}}) / ({{mvar\|T}} × {{mvar\|C}}) × 100 = ({{mvar\|S}} × {{mvar\|I}} ) × 100. <br />This implies that the ~~ratio–to~~-moving average represents the seasonal and irregular components.		# Express each original data value of the time-series as a percentage of the corresponding centered [[moving average]] values obtained in step(1). In other words, in a multiplicative time-series model, we get (Original data values) / (Trend values) × 100 = ({{mvar\|T}} × {{mvar\|C}} × {{mvar\|S}} × {{mvar\|I}}) / ({{mvar\|T}} × {{mvar\|C}}) × 100 = ({{mvar\|S}} × {{mvar\|I}} ) × 100. <br />This implies that the ratio-to-moving average represents the seasonal and irregular components.
	#:		#:
	#:		#:

InternetArchiveBot: Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.3) (Eastmain - 13311

2023-04-19T04:50:11Z

Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.3) (Eastmain - 13311

← Previous revision		Revision as of 04:50, 19 April 2023
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	{{More citations needed\|date=November 2010}}		{{More citations needed\|date=November 2010}}
	}}		}}
	In [[time series]] data, '''seasonality''' is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html}}</ref> of a time series.		In [[time series]] data, '''seasonality''' is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html\|title=Archived copy\|access-date=2015-05-13\|archive-date=2015-05-18\|archive-url=https://web.archive.org/web/20150518104248/http://www.businessdictionary.com/definition/seasonality.html\|url-status=dead}}</ref> of a time series.

	Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>		Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>

2001:569:FBBC:FD00:7F8D:AD62:A8BD:DF60: /* Modeling */

2023-03-07T03:57:29Z

Modeling

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	Regression With Fourier Terms:		Regression With Fourier Terms:
	:<math>Y_i = a + bt + (\sum_{k=1}^K \alpha_k\cdot \sin(\tfrac{2\pi kt}{m}) + \beta_k\cdot \cos(\tfrac{2\pi kt}{n})) + E_i </math>		:<math>Y_i = a + bt + (\sum_{k=1}^K \alpha_k\cdot \sin(\tfrac{2\pi kt}{m}) + \beta_k\cdot \cos(\tfrac{2\pi kt}{m})) + E_i </math>

	==Seasonal adjustment==		==Seasonal adjustment==

Citation bot: Alter: template type. Add: title. | Use this bot. Report bugs. | Suggested by BrownHairedGirl | #UCB_webform 57/3609

2022-06-10T02:06:41Z

Alter: template type. Add: title. | Use this bot. Report bugs. | Suggested by BrownHairedGirl | #UCB_webform 57/3609

← Previous revision		Revision as of 02:06, 10 June 2022
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	{{More citations needed\|date=November 2010}}		{{More citations needed\|date=November 2010}}
	}}		}}
	In [[time series]] data, '''seasonality''' is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html}} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html}}</ref> of a time series.		In [[time series]] data, '''seasonality''' is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, vacation, and holidays<ref>{{cite web\|url=http://www.allbusiness.com/barrons_dictionary/dictionary-seasonality-4946957-1.html\|title=Seasonality }} \|title=Influencing Factors\|</ref> and consists of periodic, repetitive, and generally regular and predictable patterns in the levels<ref>{{cite web\|url=http://www.businessdictionary.com/definition/seasonality.html}}</ref> of a time series.

	Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite ~~web~~\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>		Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period. Such non-seasonal fluctuations are usually due to economic conditions and are often related to the "business cycle"; their period usually extends beyond a single year, and the fluctuations are usually of at least two years.<ref name="otexts.org">{{cite book\|url=https://www.otexts.org/fpp/6/1\|title=6.1 Time series components - OTexts}}</ref>

	Organisations facing seasonal variations, such as ice-cream vendors, are often interested in knowing their performance relative to the normal seasonal variation. Seasonal variations in the labour market can be attributed to the entrance of school leavers into the job market as they aim to contribute to the workforce upon the completion of their schooling. These regular changes are of less interest to those who study employment data than the variations that occur due to the underlying state of the economy; their focus is on how unemployment in the workforce has changed, despite the impact of the regular seasonal variations.<ref name="otexts.org"/>		Organisations facing seasonal variations, such as ice-cream vendors, are often interested in knowing their performance relative to the normal seasonal variation. Seasonal variations in the labour market can be attributed to the entrance of school leavers into the job market as they aim to contribute to the workforce upon the completion of their schooling. These regular changes are of less interest to those who study employment data than the variations that occur due to the underlying state of the economy; their focus is on how unemployment in the workforce has changed, despite the impact of the regular seasonal variations.<ref name="otexts.org"/>
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	The following [[graphical technique]]s can be used to detect seasonality:		The following [[graphical technique]]s can be used to detect seasonality:
	* A [[run sequence plot]] will often show seasonality [[File:SeasonalplotUS.png\|thumb\|A seasonality plot of US electricity usage]]		* A [[run sequence plot]] will often show seasonality [[File:SeasonalplotUS.png\|thumb\|A seasonality plot of US electricity usage]]
	* A seasonal plot will show the data from each season overlapped<ref name="auto">{{cite ~~web~~\|url=https://www.otexts.org/fpp/2/1\|title=2.1 Graphics - OTexts}}</ref>		* A seasonal plot will show the data from each season overlapped<ref name="auto">{{cite book\|url=https://www.otexts.org/fpp/2/1\|title=2.1 Graphics - OTexts}}</ref>
	* A [[seasonal subseries plot]] is a specialized technique for showing seasonality		* A [[seasonal subseries plot]] is a specialized technique for showing seasonality
	* Multiple [[box plot]]s can be used as an alternative to the seasonal subseries plot to detect seasonality		* Multiple [[box plot]]s can be used as an alternative to the seasonal subseries plot to detect seasonality

← Previous revision		Revision as of 04:49, 13 June 2024
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			{{Short description\|Variations in data at specific regular intervals less than a year}}
	{{Multiple issues\|		{{Multiple issues\|
	{{More footnotes\|date=November 2008}}		{{More footnotes\|date=November 2008}}

← Previous revision		Revision as of 23:13, 13 April 2024
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	==See also==		==See also==
			*[[Box–Jenkins method]]
	*[[Oscillation]]		*[[Oscillation]]
	*[[Periodic function]]		*[[Periodic function]]