The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices
Open accessAbstract
Stock price prediction has always attracted interest because of the direct financial benefit and the associated complexity.From our literature review, we felt the need of a study having sector specific analysis with a broad range of stocks.In this paper, we have conducted a study on the effectiveness of Autoregressive Integrated Moving Average (ARIMA)model, on fifty six Indian stocks from different sectors.We have chosen ARIMA model, because of its simplicity and wide acceptability of the model.We also have studied the effect on prediction accuracy based on various possible previous period data taken. The comparison and parameterization of the ARIMA model have been done using Akaike information criterion (AIC). The contribution of the paper , are a) coverage of a good number of Indian stocks b) Analysis of the models based on sectors c) Analysis of prediction accuracy based on the varying span of previous period data.
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R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Willmott, C., & Matsuura, K. (2005) · Climate Research · doi:10.3354/cr030079 · Cited by 6,512
The Analysis of Time Series: An Introduction
Dickey, D. A., & Chatfield, C. (1991) · Technometrics · doi:10.2307/1268794 · Cited by 3,525
Intervention Analysis with Applications to Economic and Environmental Problems
Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10480264 · Cited by 2,383
Model Based Inference in the Life Sciences: A Primer on Evidence
Anderson, D. R. (2008) · Springer eBooks · doi:10.1007/978-0-387-74075-1 · Cited by 1,841
ARIMA models to predict next-day electricity prices
Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.804943 · Cited by 1,553
ARIMA Models to Predict Next-Day Electricity Prices
Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2002) · IEEE Power Engineering Review · doi:10.1109/mper.2002.4312577 · Cited by 928
The Analysis of Time Series: An Introduction
Chatfield, C. (1980) · Springer eBooks · doi:10.1007/978-1-4899-2923-5 · Cited by 784
Yang, Y. (2005) · Biometrika · doi:10.1093/biomet/92.4.937 · Cited by 605
Forecasting With Dynamic Regression Models.
Shumway, R. H., & Pankratz, A. (1993) · Journal of the American Statistical Association · doi:10.2307/2290369 · Cited by 378
Cited by
Stock-Price Forecasting Based on XGBoost and LSTM
Hoang Vuong, P., Tan Dat, T., Khoi Mai, T., Hoang Uyen, P., & The Bao, P. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2022.017685 · Cited by 100
Stock Market Increase and Decrease using Twitter Sentiment Analysis and ARIMA Model
Kedar, S. V. (2021) · Turkish Journal of Computer and Mathematics Education (TURCOMAT) · doi:10.17762/turcomat.v12i1s.1596 · Cited by 10
Metadata
- Authors
- Mondal, P., Shit, L., & Goswami, S.
- Venue
- International Journal of Computer Science, Engineering and Applications
- Published
- 2014-04-30
- Volume
- 4
- Issue
- 2
- Pages
- 13-29
- ISSN
- 2230-9616 (ISSN-L)
- 2231-0088
- OpenAlex
- W2331820705
- Cited by
- 284
- References
- 16
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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