Time series analysis of NASDAQ composite based on seasonal ARIMA model

Wang Weiqiang; Niu Zhendong

doi:10.1109/ICMSS.2009.5300866

Time series analysis of NASDAQ composite based on seasonal ARIMA model

Wang Weiqiang^*, Niu Zhendong

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, A time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation,diagnostic checking procedures to model, predict NASDAQ data extracted from yahoo website using seasonal ARIMA models, and also compare with other models. we show experimental results with NASDAQ data sets indicate that the seasonal ARIMA model can be an effective way to forecast finance.

Original language	English
Title of host publication	Proceedings - International Conference on Management and Service Science, MASS 2009
DOIs	https://doi.org/10.1109/ICMSS.2009.5300866
Publication status	Published - 2009
Event	International Conference on Management and Service Science, MASS 2009 - Wuhan, China Duration: 20 Sept 2009 → 22 Sept 2009

Publication series

Name	Proceedings - International Conference on Management and Service Science, MASS 2009

Conference

Conference	International Conference on Management and Service Science, MASS 2009
Country/Territory	China
City	Wuhan
Period	20/09/09 → 22/09/09

Keywords

Nasdaq
Seasonal ARIMA
Time series

Access to Document

10.1109/ICMSS.2009.5300866

Cite this

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title = "Time series analysis of NASDAQ composite based on seasonal ARIMA model",

abstract = "An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, A time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation,diagnostic checking procedures to model, predict NASDAQ data extracted from yahoo website using seasonal ARIMA models, and also compare with other models. we show experimental results with NASDAQ data sets indicate that the seasonal ARIMA model can be an effective way to forecast finance.",

keywords = "Nasdaq, Seasonal ARIMA, Time series",

author = "Wang Weiqiang and Niu Zhendong",

year = "2009",

doi = "10.1109/ICMSS.2009.5300866",

language = "English",

isbn = "9781424446391",

series = "Proceedings - International Conference on Management and Service Science, MASS 2009",

booktitle = "Proceedings - International Conference on Management and Service Science, MASS 2009",

note = "International Conference on Management and Service Science, MASS 2009 ; Conference date: 20-09-2009 Through 22-09-2009",

}

Weiqiang, W & Zhendong, N 2009, Time series analysis of NASDAQ composite based on seasonal ARIMA model. in Proceedings - International Conference on Management and Service Science, MASS 2009., 5300866, Proceedings - International Conference on Management and Service Science, MASS 2009, International Conference on Management and Service Science, MASS 2009, Wuhan, China, 20/09/09. https://doi.org/10.1109/ICMSS.2009.5300866

Time series analysis of NASDAQ composite based on seasonal ARIMA model. / Weiqiang, Wang; Zhendong, Niu.
Proceedings - International Conference on Management and Service Science, MASS 2009. 2009. 5300866 (Proceedings - International Conference on Management and Service Science, MASS 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhendong, Niu

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N2 - An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, A time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation,diagnostic checking procedures to model, predict NASDAQ data extracted from yahoo website using seasonal ARIMA models, and also compare with other models. we show experimental results with NASDAQ data sets indicate that the seasonal ARIMA model can be an effective way to forecast finance.

AB - An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, A time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation,diagnostic checking procedures to model, predict NASDAQ data extracted from yahoo website using seasonal ARIMA models, and also compare with other models. we show experimental results with NASDAQ data sets indicate that the seasonal ARIMA model can be an effective way to forecast finance.

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KW - Seasonal ARIMA

KW - Time series

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Time series analysis of NASDAQ composite based on seasonal ARIMA model

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Keywords

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