Redes neuronales - página 24

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Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.


 
La construcción de un modelo de red neuronal computacional feedforward (CNN) implica dos tareas distintas: la determinación de la topología de la red y la estimación de los pesos. La especificación de una topología de red adecuada al problema es una cuestión clave y el objetivo principal de esta contribución. Hasta ahora, esta cuestión se ha descuidado por completo en los dominios de aplicación espacial, o se ha abordado mediante heurísticas de búsqueda (véase Fischer y Gopal 1994). Con el fin de modelar las interacciones en el espacio geográfico, este trabajo considera este problema como un problema de optimización global y propone un enfoque novedoso que integra el aprendizaje por retropropagación en el paradigma evolutivo de los algoritmos genéticos. Esto se consigue entrelazando una búsqueda genética para encontrar una topología óptima de la CNN con el aprendizaje por retropropagación basado en el gradiente para determinar los parámetros de la red. De este modo, el constructor del modelo se verá liberado de la carga de identificar topologías de CNN adecuadas que permitan resolver un problema con mecanismos de aprendizaje sencillos, pero potentes, como la retropropagación de errores de descenso de gradiente. El enfoque se ha aplicado a la familia de modelos CNN feedforward de tres entradas, una sola capa oculta y una sola salida, utilizando datos de tráfico de telecomunicaciones interregionales de Austria, para ilustrar su rendimiento y evaluar su robustez.
 
Las redes neuronales (NN) son herramientas no lineales de la rama de inteligencia artificial de la informática que pueden utilizarse en el análisis financiero y la previsión, especialmente para las predicciones a corto plazo. Ofrecen una alternativa útil a los métodos tradicionales, como el análisis discriminante y la regresión, especialmente cuando se exploran patrones no lineales o desconocidos en conjuntos de datos masivos, a veces incompletos. Las potentes NN tienen importantes limitaciones. Los resultados a veces no son robustos, sino que dependen del entrenamiento, y son difíciles de replicar. La clasificación de categorías adyacentes, como las calificaciones de los bonos, suele estar sujeta a altos índices de error. Existen programas informáticos para procesar las NN, pero su aplicación puede resultar larga y costosa en función del tamaño del conjunto de datos y de la complejidad de la NN.
 
La fiabilidad es un problema bien conocido en los entornos de HPC actuales y se espera que se convierta en un reto aún mayor en la próxima generación de sistemas a escala peta. Dado que los enfoques actuales de tolerancia a los fallos (por ejemplo, los mecanismos de comprobación/reinicio) se consideran ineficaces debido a los problemas de rendimiento y escalabilidad, actualmente se están investigando enfoques de tolerancia a los fallos mejorados, como la evitación proactiva de fallos (PFA). El enfoque PFA se basa en la predicción y migración de fallos para reducir tanto el impacto de los fallos en las aplicaciones como el tiempo de recuperación. En este documento, exploramos el uso de técnicas de redes neuronales artificiales (RNA) para mejorar la predicción de fallos en un contexto de PFA. Entrenando inicialmente la red feed-forward con un algoritmo de aprendizaje de retropropagación supervisado, esta red es alimentada con datos históricos de sensores IPMI recogidos de nuestro cluster. Los resultados muestran una mejora del rendimiento de la predicción con respecto al enfoque anterior de "activación de umbrales".
 

Stock market decision making is a very challenging and difficult task of �financial data prediction. Prediction about stock market with high accuracy movement yield pro�fit for investors of the stocks. Because of the complexity of stock market �financial data, development of efficient models for prediction decision is very difficult and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Arti�cial Neural Network(ANN) and Random Forest has been used for developing of prediction model. Technical indicators are calculated from the stock prices based on time-line data and it is used as inputs of the proposed prediction models. Ten years of stock market data has been used for signal prediction of stock. Based on the data set, these models are capable to generate buy/hold signal for stock market as a output. The main goal of this project is to generate output signal(buy/hold) as per users requirement like amount to be invested, time duration for investment, minimum profit, maximum loss using data mining and machine learning techniques.



 

In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.


 

The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.


 
Estos diagramas exploran el uso de la noción de red relacional de Sydney Lamb para la lingüística con el fin de representar la estructura lógica de una colección compleja de paisajes atractores (como en el relato de Walter Freeman sobre la neurodinámica). Dado un sistema suficientemente grande, como un sistema nervioso vertebrado, se podría pensar que el atractor net es en sí mismo un sistema dinámico, uno de un orden superior al de los sistemas dinámicos realizados a nivel neuronal. Entre las construcciones se encuentran: la variedad (la herencia "es-una"), los movimientos simples, el conteo y la notación de lugares, la orientación en el tiempo y el espacio, el lenguaje y el aprendizaje.
 

Effectiveness of the use of neural-net technology for the solving of shell theory problems is shown. Some results of neural-net interpolation and extrapolation for direct and inverse problems are discussed. Exact accuracy of neural-net solving opens wide latitude for shell constructions engineering design and optimization.


 

The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.


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