1,596 research outputs found
A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information
Time series forecasting represents a significant and challenging task across
various fields. Recently, methods based on mode decomposition have dominated
the forecasting of complex time series because of the advantages of capturing
local characteristics and extracting intrinsic modes from data. Unfortunately,
most models fail to capture the implied volatilities that contain significant
information. To enhance the prediction of contemporary diverse and complex time
series, we propose a novel time series forecasting paradigm that integrates
decomposition with the capability to capture the underlying fluctuation
information of the series. In our methodology, we implement the Variational
Mode Decomposition algorithm to decompose the time series into K distinct
sub-modes. Following this decomposition, we apply the Generalized
Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the
volatility information in these sub-modes. Subsequently, both the numerical
data and the volatility information for each sub-mode are harnessed to train a
neural network. This network is adept at predicting the information of the
sub-modes, and we aggregate the predictions of all sub-modes to generate the
final output. By integrating econometric and artificial intelligence methods,
and taking into account both the numerical and volatility information of the
time series, our proposed framework demonstrates superior performance in time
series forecasting, as evidenced by the significant decrease in MSE, RMSE, and
MAPE in our comparative experimental results
Pronóstico del precio de cobre utilizando técnicas de aprendizaje profundo
Pronosticar los precios futuros de cobre es una tarea desafiante dadas las características dinámicas y no lineales de varios factores que afectan el precio del cobre. Este artículo describe modelos de pronóstico, basados en arquitecturas de redes neuronales, para predecir los retornos del precio de cobre en tres horizontes de tiempo: un día, una semana y un mes adelante. Diversas variables se consideran como variables de entrada, como los precios históricos de diferentes materias primas metálicas y variables macroeconómicas globales. Evaluamos los modelos con datos diarios de 2007 a 2020. Los resultados experimentales mostraron que los modelos de salida única presentan un mejor rendimiento predictivo que los modelos de salida múltiple. Las arquitecturas de mejor rendimiento fueron los modelos de memorias largas a corto plazo (LSTM) en datos de prueba.Forecasting the future prices of copper commodity is a challenging task given the
dynamic and non-linear characteristics of various factors that affect the copper price.
This article describes forecasting models, based on neural network architectures, to
predict copper price returns at three time horizons: one-day, one-week, and onemonth
ahead. Several variables are considered as input variables, like historical prices
of different metallic commodities and global macroeconomic variables. We evaluated
the models with daily data from 2007 to 2020. The experimental results showed
that mono-output models present better predictive performance than multi-output
models. The best-performing architectures were the Long Short-Term Memories
(LSTM) models on test data
From Deep Filtering to Deep Econometrics
Calculating true volatility is an essential task for option pricing and risk
management. However, it is made difficult by market microstructure noise.
Particle filtering has been proposed to solve this problem as it favorable
statistical properties, but relies on assumptions about underlying market
dynamics. Machine learning methods have also been proposed but lack
interpretability, and often lag in performance. In this paper we implement the
SV-PF-RNN: a hybrid neural network and particle filter architecture. Our
SV-PF-RNN is designed specifically with stochastic volatility estimation in
mind. We then show that it can improve on the performance of a basic particle
filter
An empirical study on the various stock market prediction methods
Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods
latent Dirichlet allocation method-based nowcasting approach for prediction of silver price
Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method
Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks.
Política de acceso abierto tomada de: https://www.aimspress.com/index/news/solo-detail/openaccesspolicyThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of
Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new
cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like
Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In
this article, we contribute a comparative analysis encompassing deep learning and quantum methods
within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH
(Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In
this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell”
decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our
findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and
precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum
Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency
consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive
strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential
of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing
risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This
research provides insights for investors, regulators, and developers in the cryptocurrency market.
Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural
network models for enhanced analysis.This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634
A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases
Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other
hand, the cmABC algorithm that incorporates conventional mutation addresses the over-
fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non
renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical
metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error
(RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the
prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit
Developing a hybrid hidden MARKOV model using fusion of ARMA model and artificial neural network for crude oil price forecasting
Crude oil price forecasting is an important component of sustainable development of
many countries as crude oil is an unavoidable product that exist on earth. Crude oil
price forecasting plays a very vital role in economic development of many countries
in the world today. Any fluctuation in crude oil price tremendously affects many
economies in terms of budget and expenditure. In view of this, it is of great concern
by economists and financial analysts to forecast such a vital commodity. However,
Hidden Markov Model, ARMA Model and Artificial Neural Network has many
drawbacks in forecasting such as linear limitations of ARMA model which is in
contrast to the financial time series which are often nonlinear, ANN is very weak in
terms of out-sample forecast and it has very tedious process of implementation, HMM
is very weak in an in-sample forecast and has issue of a large number of unstructured
parameters. In view of this drawbacks of these three models (ANN, ARMA and
HMM), we developed an efficient Hybrid Hidden Markov Model using fusion of
ARMA Model and Artificial Neural Network for crude oil price forecasting,
MATLAB was employed to develop the four models (Hybrid HMM, HMM, ARMA
and ANN). The models were evaluated using three different evaluation techniques
which are Mean Absolute Percentage Error (MAPE), Absolute Error (AE) and Root
Mean Square Error (RMSE). The findings showed that Hybrid Hidden Markov Model
was found to provide more accurate crude oil price forecast than the other three
models in which. The results of this study indicate that Hybrid Hidden Markov Model
using fusion of ARMA and ANN is a potentially promising model for crude oil price
forecasting
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