5,636 research outputs found
Online Learning in Case of Unbounded Losses Using the Follow Perturbed Leader Algorithm
In this paper the sequential prediction problem with expert advice is
considered for the case where losses of experts suffered at each step cannot be
bounded in advance. We present some modification of Kalai and Vempala algorithm
of following the perturbed leader where weights depend on past losses of the
experts. New notions of a volume and a scaled fluctuation of a game are
introduced. We present a probabilistic algorithm protected from unrestrictedly
large one-step losses. This algorithm has the optimal performance in the case
when the scaled fluctuations of one-step losses of experts of the pool tend to
zero.Comment: 31 pages, 3 figure
Predicting Daily Probability Distributions Of S&P500 Returns
Most approaches in forecasting merely try to predict the next value of the time series.
In contrast, this paper presents a framework to predict the full probability distribution. It
is expressed as a mixture model: the dynamics of the individual states is modeled with so-called
"experts" (potentially nonlinear neural networks), and the dynamics between the states is modeled
using a hidden Markov approach. The full density predictions are obtained by a weighted superposition
of the individual densities of each expert. This model class is called "hidden Markov experts".
Results are presented for daily S&P500 data. While the predictive accuracy of the mean does
not improve over simpler models, evaluating the prediction of the full density shows a clear out-of-sample
improvement both over a simple GARCH(1,l) model (which assumes Gaussian distributed
returns) and over a "gated experts" model (which expresses the weighting for each state non-recursively
as a function of external inputs). Several interpretations are given: the blending of
supervised and unsupervised learning, the discovery of hidden states, the combination of forecasts,
the specialization of experts, the removal of outliers, and the persistence of volatility.Information Systems Working Papers Serie
Discrete representation strategies for foreign exchange prediction
This is an extended version of the paper presented at the 4th International Workshop NFMCP 2015 held in conjunction with ECML PKDD 2015. The initial version has been published in NFMCP 2015 conference proceedings as part of Springer Series. This paper presents a novel approach to financial times series (FTS) prediction by mapping hourly foreign exchange data to string representations and deriving simple trading strategies from them. To measure the degree of similarity in these market strings we apply familiar string kernels, bag of words and n-grams, whilst also introducing a new kernel, time-decay n-grams, that captures the temporal nature of FTS. In the process we propose a sequential Parzen windows algorithm based on discrete representations where trading decisions for each string are learned in an online manner and are thus subject to temporal fluctuations. We evaluate the strength of a number of representations using both the string version and its continuous counterpart, whilst also comparing the performance of different learning algorithms on these representations, namely support vector machines, Parzen windows and Fisher discriminant analysis. Our extensive experiments show that the simple string representation coupled with the sequential Parzen windows approach is capable of outperforming other more exotic approaches, supporting the idea that when it comes to working in high noise environments often the simplest approach is the most effective
Métodos para la previsión de los precios del gas
The difficulty in gas price forecasting has attracted much attention of academic
researchers and business practitioners. Various methods have been tried to solve the problem of
forecasting gas prices however, all of the existing models of prediction cannot meet practical needs.
In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration
of GMDH neural networks with GA and Rule-based Exert System (RES) employs for gas
price forecasting. In this paper we use a new method for extract the rules and compare different
methods for gas price forecasting.
Our research reveals that during the recent financial crisis period by employing hybrid intelligent
framework for gas price forecasting, we obtain better forecasting results compared to the
GMDH neural networks and MLF neural networks and results will be so better when we employ hybrid intelligent system with for gas price volatility forecastingLa dificultad de la previsión de los precios del gas ha atraído considerablemente la atención de
los investigadores universitarios y los profesionales del sector. A pesar de que se ha intentado
solucionar el problema de la previsión de los precios del gas con diferentes métodos, ninguno de
los modelos de predicción existentes llegan a cumplir con las necesidades prácticas.
En este artículo, se ha desarrollado un novedoso sistema inteligente híbrido mediante la
aplicación de la integración sistemática de redes neuronales de tipo Group Method of Data
Handling (GMDH) con algoritmos genéticos (AG) y un sistema experto basado en reglas (SER) a la previsión de los precios del gas. Igualmente, utilizamos un nuevo método para extraer las reglas
y comparar los diferentes métodos para la previsión de los precios del gas.
Nuestra investigación revela que durante la reciente crisis económica se obtienen mejores
resultados utilizando un sistema inteligente híbrido para la previsión de los precios del gas, en
comparación con las redes neuronales de tipo GMDH y de tipo Multi-Layer Feed-forward (MLF),
y que los resultados mejorarán si utilizamos un sistema inteligente híbrido en la previsión
de la volatilidad de los precios del ga
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