Pages that link to "Item:Q2387269"

The following pages link to Forecasting stock market movement direction with support vector machine (Q2387269):

Displaying 36 items.

• An application of support vector machines to sales forecasting under promotions (Q330115) (← links)
• A multiobjective based approach for mathematical programs with linear flexible constraints (Q345535) (← links)
• Forecasting urban traffic flow by SVR with continuous ACO (Q534897) (← links)
• Forecasting of stock returns by using manifold wavelet support vector machine (Q615302) (← links)
• Hybrid evolutionary algorithms in a SVR traffic flow forecasting model (Q632928) (← links)
• Prediction of cryptocurrency returns using machine learning (Q829124) (← links)
• A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts (Q856084) (← links)
• Composite leading search index: a preprocessing method of internet search data for stock trends prediction (Q893052) (← links)
• Financial market forecasting using a two-step kernel learning method for the support vector regression (Q970173) (← links)
• Modified neural network algorithms for predicting trading signals of stock market indices (Q1040018) (← links)
• Forecasting NIKKEI 225 index with support vector machine (Q1433515) (← links)
• Financial forecasting using support vector machines (Q1606650) (← links)
• A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach (Q1761095) (← links)
• Stock market prediction and portfolio selection models: a survey (Q1788855) (← links)
• News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions (Q1991118) (← links)
• Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations (Q2143016) (← links)
• Forecasting the movement direction of exchange rate with polynomial smooth support vector machine (Q2256445) (← links)
• Generalized high-dimensional trace regression via nuclear norm regularization (Q2323374) (← links)
• Forecasting US stock market returns: a Japanese candlestick approach (Q2661905) (← links)
• Large dynamic covariance matrix estimation with an application to portfolio allocation: a semiparametric reproducing kernel Hilbert space approach (Q2674937) (← links)
• Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling (Q3019211) (← links)
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• (Q3611454) (← links)
• Binary switch portfolio (Q4555108) (← links)
• (Q4996348) (← links)
• Prediction of stock price movement based on daily high prices (Q5001173) (← links)
• Stock market prediction based on adaptive training algorithm in machine learning (Q5079405) (← links)
• Predicting credit card customer churn using support vector machine based on Bayesian optimization (Q5083788) (← links)
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• Improving TAIEX forecasting using fuzzy time series with Box–Cox power transformation (Q5129123) (← links)
• The Signature Kernel Is the Solution of a Goursat PDE (Q5162619) (← links)
• Support Vector Clustering for Customer Segmentation on Mobile TV Service (Q5265804) (← links)
• Nonlinear support vector machines can systematically identify stocks with high and low future returns (Q5420705) (← links)
• A two-step machine learning approach to predict S&P 500 bubbles (Q5861221) (← links)
• A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms (Q6105931) (← links)
• Time series modeling and forecasting by mathematical programming (Q6109289) (← links)