Journal ArticleDOI
Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
Manish Kumar,M. Thenmozhi +1 more
Reads0
Chats0
TLDR
The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.Abstract:
The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.read more
Citations
More filters
Journal ArticleDOI
A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting
Zahra Rahimi,Mehdi Khashei +1 more
TL;DR: A new parallel hybrid model is proposed, incorporating autoregressive integrated moving average (ARIMA) and multilayer perceptrons (MLPs), which are one of the most important intelligent and statistical time series models, and the computational cost of the proposed model is significantly lower than the GAHM and roughly equal to the SAHM.
Journal ArticleDOI
A multi-method patient arrival forecasting outline for hospital emergency departments
TL;DR: A novel attempt of applying single methods as linear regression, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), exponential smoothing and hybrid methods to model ED patient arrivals and making an overall assessment among them is attempted.
Journal ArticleDOI
A hybrid ARIMA–ANN method to forecast daily global solar radiation in three different cities in Morocco
TL;DR: The proposed hybrid model to forecasted the daily global solar radiation (DGSR) in three different cities in Morocco shows a high correlation with experimental results and a relatively small error rate, and can be used in forecasting photovoltaic power or temperature.
Journal ArticleDOI
Financial Trading Strategy System Based on Machine Learning
TL;DR: A new financial trading strategy system is provided by introducing Light Gradient Boosting Machine (LightGBM) algorithm into stock price prediction and by constructing the minimum variance portfolio of mean-variance model with Conditional Value at Risk (CVaR) constraint.
Proceedings ArticleDOI
Forecasting Malaysian exchange rate using machine learning techniques based on commodities prices
TL;DR: In this article, the authors investigated the dynamic interactions between four commodities prices and the exchange rate for an emerging economy, Malaysia, and provided a new methodology to perform a comparative analysis of the three machine learning techniques, namely: Support Vector Machine, Neural Networks, and RandomForest.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Forecasting with artificial neural networks: the state of the art
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.
Journal ArticleDOI
Time series forecasting using a hybrid ARIMA and neural network model
TL;DR: Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
Journal ArticleDOI
Combining forecasts: A review and annotated bibliography
TL;DR: In this article, the authors provide a review and annotated bibliography of that literature, including contributions from the forecasting, psychology, statistics, and management science literatures, providing a guide to the literature for students and researchers and to help researchers locate contributions in specific areas, both theoretical and applied.
Journal ArticleDOI
Financial time series forecasting using support vector machines
TL;DR: The experimental results show that SVM provides a promising alternative to stock market prediction and the feasibility of applying SVM in financial forecasting is examined by comparing it with back-propagation neural networks and case-based reasoning.
Related Papers (5)
A hybrid ARIMA and support vector machines model in stock price forecasting
Ping-Feng Pai,Chih-Sheng Lin +1 more