Journal ArticleDOI
Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
Manish Kumar,M. Thenmozhi +1 more
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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
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Book ChapterDOI
A Literature Review on Machine Learning Techniques and Strategies Applied to Stock Market Price Prediction
TL;DR: In this article, a survey of more than 100 research papers proposing the various philosophies of machine learning methods as well as SVM classifier, Random Forest, Neural Network, Bayesian model, Fuzzy classifiers, Artificial Neural Networks, etc., in view of stock market price prediction is presented.
Journal ArticleDOI
Method for predicting depth-averaged current velocities of underwater gliders based on data feature analysis
TL;DR: The results show that the prediction methods proposed in this paper are effective and based on three general error criteria, the prediction performance of the proposed model is demonstrated.
Proceedings ArticleDOI
Time Series Analysis Using Stacked LSTM Model for Indian Stock Market
TL;DR: In this article , the authors used Long Short-Term Memory (LSTM) and a deep learning model to predict the stock price of five NIFTY 50 shares and then compared it with the actual value to analyze the accuracy of price prediction.
Journal ArticleDOI
Forecasting Tanker Indices with ARIMA-SVM Hybrid Models
Sangseop Lim,Hee-Sung Yun +1 more
Impact of Technical Indicators and Leading Indicators on Stock Trends on the Internet of Things
TL;DR: A new stock trend prediction framework is used to predict changes in the stock price direction on the next trading day using data from the past 30 trading days and results show that the model’s profitability is better than the two baseline strategies.
References
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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.
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