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Volatility forecasting using time series data mining and evolutionary computation techniques
2007
Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07
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The current work applies the genetic programming in a Time Series Data Mining framework to characterize the S&P100 high frequency data in order to forecast the one step ahead integrated volatility. ...
Recent advance in evolutionary computation has provided additional tools to conduct data mining effectively. ...
The first 21 days of both sets of data are used to prepare for the 21-day moving average, in order to take the monthly effect into consideration, to de-trend and to improve the forecasting accuracy. ...
doi:10.1145/1276958.1277397
dblp:conf/gecco/MaWS07
fatcat:zbd6ja56o5h6ljysrj7ujq6msu
A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction
2017
Journal of ICT Research and Applications
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Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. ...
Time series data available in huge amounts can be used in decisionmaking. Such time series data can be converted into information to be used for forecasting. ...
It was clearly observed that the use of soft computing techniques and evolutionary algorithms can also be applied successfully in time series data analysis. ...
doi:10.5614/itbj.ict.res.appl.2017.11.2.4
fatcat:nmqzd54wtfdihpwsgsgtz7oub4
A Review on Prediction of Stock Market using Various Methods in the Field of Data Mining
2016
Indian Journal of Science and Technology
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This survey helps to know which technique is the best to use in the field of predicting stock market in the area of mining. ...
Stock prediction can be done by using the current and previous data available on the market. ...
In 12 used PSO to predict the stock volatility with less computational complexity and high accuracy. ...
doi:10.17485/ijst/2016/v9i48/107985
fatcat:qrcy4eyytbh6rhzx3g4bsphkwy
Page 1171 of The Journal of the Operational Research Society Vol. 59, Issue 9
[page]
2008
The Journal of the Operational Research Society
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
Experience with forecasting univariate time series and the combination of forecasts. J R Statist Soc (A) 137: 131-164.
Olafsson S (2006). Introduction to operations research and data mining. ...
A comparison of forecasting techniques on economic time series. In: Bramson MJ, Helps IG and Watson-Gandy JACC (eds). Forecasting in Action. ...
Oil price forecasting using gene expression programming and artificial neural networks
2016
Economic Modelling
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a r t i c l e i n f o This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over ...
The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. ...
Acknowledgment The authors would like to thank an anonymous referee and the editor for insightful comments on a previous version of this paper. ...
doi:10.1016/j.econmod.2015.12.014
fatcat:vqsmpgz3szffdhyzy6ih4eljau
Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming
2009
Journal of Applied Mathematics and Decision Sciences
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By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes ...
Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. ...
Applying a combination of theory and techniques such as wavelet transform, time series data mining, Markov chain-based discrete stochastic optimization, and evolutionary algorithms GA and GP, Ma et al ...
doi:10.1155/2009/179230
fatcat:jguuh342bfd3lk2av3qoj2ba7m
Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm
2011
Applied Soft Computing
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First, the wavelet transform using the Haar wavelet is applied to decompose the stock price time series and thus eliminate noise. ...
As these simulation results demonstrate, the proposed system is highly promising and can be implemented in a real-time trading system for forecasting stock prices and maximizing profits. Crown ...
evolutionary computation methods [28] [29] [30] [31] . ...
doi:10.1016/j.asoc.2010.09.007
fatcat:guzirywcvfezljlq4gyoucn5ka
Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks
2015
International Journal of Computational Intelligence Systems
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A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. ...
Further a recurrent version of the Functional link neural network is used to model more accurately a chaotic time series like stock market indices with a lesser number of nonlinear basis functions. ...
The accuracy and robustness of
forecasting the stock prices and volatilities can,
however, be improved further by optimizing the weights
of the PFALRNN by using some evolutionary
techniques like GA ...
doi:10.1080/18756891.2015.1099910
fatcat:lrh5jyc4dbcvrbrxdr3zktkhwq
The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange
[article]
2010
arXiv
pre-print
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It is used Linear Regression and Artificial Neural Network methods and compared these two methods. ...
to estimate the stock price using Independent components Analysis (ICA). ...
An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28, 1183-1202. ...
arXiv:1003.1457v2
fatcat:fe7sanx445euhhhbesf6v7qhiu
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
[article]
2019
arXiv
pre-print
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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas ...
As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. ...
data at the same time; studies using text mining techniques and studies using other various data. ...
arXiv:1911.13288v1
fatcat:npvyhewuvvcvri4e43jwj3c45y
THE SURVEY OF SOFT COMPUTING TECHNIQUES FOR RELIABILITY PREDICTION
2015
Journal of KONES Powertrain and Transport
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system (FRBS) or recognizing and optimizing data-derived pattern by using evolutionary algorithms. ...
Hence, the predictive (proactive) maintenance in industrial systems involves operational conditions monitoring and online forecasting the useful life of machines equipment to support the decision-making ...
Based on the historical data, RAS events and system activity reports (SARs) collected from distributed computing systems they created predictors with use time-series methods, rule-based classification ...
doi:10.5604/12314005.1138154
fatcat:rpenk3etgbc2dgsza6ula6otwq
Demand Forecasting of Short Life Cycle Products Using Data Mining Techniques
[chapter]
2020
IFIP Advances in Information and Communication Technology
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Traditional forecasting methods are inappropriate for this type of products due to the highly uncertain and volatile demand and the lack of historical sales data. ...
This paper proposes a new data mining approach based on the incremental k-means clustering algorithm and the RULES-6 rule induction classifier for forecasting the demand of short life cycle products. ...
The author wishes to thank the University of the West of England for providing a good environment, facilities and financial means to complete this paper. ...
doi:10.1007/978-3-030-49161-1_14
fatcat:ctg5oxfabnepplys4sxacxpt6y
Mining of electricity prices in energy markets using a hybrid linear ARMA and nonlinear functional link neural network trained by evolutionary unscented H-infinity filter
2017
International Journal of Information and Decision Sciences
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The studies on PJM, Spanish and Australian energy market markets exhibit excellent forecasting results over different seasonal horizons for one day ahead of time. ...
This paper presents a hybrid autoregressive moving average (ARMA) and a nonlinear functional link neural network for electricity price forecasting in an Energy market. ...
Although, time series techniques are well established to provide reasonably good forecast, their performance deteriorates due to the use of linear models in forecasting highly volatile electricity price ...
doi:10.1504/ijids.2017.082405
fatcat:ynkrjf6vb5g53ghx23r2j7tzu4
A survey on exchange rate prediction using neural network based methods
2018
International Journal of Engineering & Technology
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Forecasting exchange rate has always been in demand as it is very important for the international traders to predict how their money will perform against other currencies. ...
In this paper, we have given the performance of different network models used by researchers to predict the exchange rates of major currencies in the future. ...
Time series data is used in ARIMA model to better predict the future values. The failure be-hind time series models is it is highly nonlinear. The variance and mean of the series vary over time. ...
doi:10.14419/ijet.v7i2.6.10069
fatcat:26jr4iykpzdtzhxkzcva7v35la
A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting
2015
Energies
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In particular, this work explores the application of these techniques to time series forecasting. ...
Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study ...
-TIC-1728 and APPB813097, respectively. ...
doi:10.3390/en81112361
fatcat:rwvvm5wwhzd5bdc2ys6hxladmy
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