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Bontempi, G., Ben Taieb, S. and Le Borgne, Y.-A. (2013) Machine Learning Strategies for Time Series Forecasting. In: Aufaure, M.-A. and Zimányi, E., Eds., Business Intelligence. eBISS 2012. Lecture Notes in Business Information Processing, Vol. 138, Springer, Berlin, 62-77.
https://doi.org/10.1007/978-3-642-36318-4_3

has been cited by the following article:

• TITLE: Forecasting Stock Prices with an Integrated Approach Combining ARIMA and Machine Learning Techniques ARIMAML

  AUTHORS: Ali Abdulhafidh Ibrahim, Bilal N. Saeed, Marwa A. Fadil

  KEYWORDS: Stock Prediction, ARIMA Model, Exponential Smoothing Model, Machine Learning, ARIMAML Model

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.8, August 15, 2023

  ABSTRACT: Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.