Time series analysis and forecasting in finance: A data mining approach
1 Department of Agricultural Economics, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
2 Hult International Business School, Boston, Massachusetts, USA.
3 Department of Computer Sciences, East Tennessee State University, Tennessee, USA.
4 Ernst & Young LLC, McLean Virginia, USA.
5 Yale School of Management, New Haven Connecticut, USA.
Review
Open Access Research Journal of Science and Technology, 2023, 09(01), 075-086.
Article DOI: 10.53022/oarjst.2023.9.1.0045
Publication history:
Received on 02 July 2023; revised on 25 September 2023; accepted on 28 September 2023
Abstract:
Time series analysis and forecasting are essential methodologies in finance, playing a pivotal role in predicting market trends, evaluating economic conditions, and supporting decision-making. These methods rely on analyzing sequential data to uncover patterns, trends, and seasonal variations that drive financial phenomena. Traditional statistical models, such as ARIMA and GARCH, have long been utilized; however, their effectiveness is often constrained by assumptions like linearity and stationarity. Recent advancements in data mining techniques, including machine learning and artificial intelligence, have transformed the landscape of time series forecasting. These innovative approaches excel at handling non-linear relationships, high-dimensional data, and noise inherent in financial markets, making them indispensable for modern financial analytics. This paper scours the concept of time series analysis and data mining, examining their integration to improve forecasting accuracy. Additionally, it evaluates challenges such as data quality and computational requirements, while highlighting emerging opportunities, such as real-time forecasting and big data applications.
Keywords:
Data Mining; Forecasting Model; Linearity; Neural Network; Stationarity; Time Series
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0