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On Forecasting the Intraday Bitcoin Price using Ensemble of Variational Mode Decomposition and Generalized Additive Model

Samuel Asante Gyamerah
2020 Journal of King Saud University: Computer and Information Sciences  
This paper presents an ensemble model using variational mode decomposition (VMD) and Generalized additive model (GAM) to forecast intraday Bitcoin price.  ...  To evaluate the performance of the constructed model, it is compared with an ensemble of empirical mode decomposition (EMD) and GAM.  ...  Gyamerah, On forecasting the intraday Bitcoin price using ensemble of variational mode decomposition and generalized additive model, Journal of King Saud University -Computer and Information Sciences,  ... 
doi:10.1016/j.jksuci.2020.01.006 fatcat:f4qrde7ho5erxfoiyznx4sntxm

Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

Samuel Asante Gyamerah
2021 Complexity  
Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition  ...  VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past  ...  Reference [14] proposed an SVR model based on empirical mode decomposition (EMD) and AR for forecasting electric load.  ... 
doi:10.1155/2021/1767708 doaj:124b451bb78c498abe0155c0d52c235b fatcat:syq6iuhbpvgonf3odlfg4b2jv4

Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition

Ruoyang Chen, Bin Pan
2016 Mathematical Problems in Engineering  
Then, the CEEMD method is combined with the Particle Swarm Optimization (PSO) algorithm-based Support Vector Machine (SVM) model to forecast Chinese stock index futures prices.  ...  In this paper, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is used to decompose the sequences of Chinese stock index futures prices into residue terms, low-frequency terms, and  ...  In addition, although previous studies on stock and stock index futures markets are based on low-frequency daily, weekly, and monthly price data [1-3, 19-22, 29] , current research regarding intraday  ... 
doi:10.1155/2016/3791504 fatcat:cdde3cey2rea3eftklwu4gilz4

Image Processing Tools for Financial Time Series Classification [article]

Bairui Du, Delmiro Fernandez-Reyes, Paolo Barucca
2020 arXiv   pre-print
based on the wavelet image constructed from the price changes in the first hours of the day.  ...  A wavelet transform is applied to the log-return of stock prices for both image extraction and denoising.  ...  The main novelty and contribution of our study is to define stock market states based on intraday financial time series whilst also providing accurate predictions, demonstrating the ability of image processing  ... 
arXiv:2008.06042v2 fatcat:eadpzzd2ubgvrl73xmfm2zvapu

Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index [article]

Dhanya Jothimani, Ravi Shankar, Surendra S. Yadav
2016 arXiv   pre-print
Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models.  ...  The proposed methods (MODWT-ANN and MODWT-SVR) are compared with ANN and SVR models and, it was observed that the return on investment obtained based on trading rules using predicted values of MODWT-SVR  ...  In technical analysis, the movement of stock price is predicted based on the behavior of previous stock price values. Technical analysts focus on the market timings.  ... 
arXiv:1605.07278v1 fatcat:2wxwwopv5zcptc5nxj54awao4e

Dynamic Interactions between Intraday Returns and Trading Volume on the CSI 300 Index Futures: An Application of an SVAR Model

Susheng Wang, Guanglu Li, Junbo Wang
2019 Mathematical Problems in Engineering  
The results of data description using ten samples of high-frequency data to describe the intraday characteristics of the CSI 300 index futures show that there is no significant summit and fat tail phenomenon  ...  Subsequent variance decomposition results show that the residual disturbance of returns can be explained more than 99.9% by its lagged terms; the residual disturbance of trading volume explained by its  ...  Forecasting Error Variance Decomposition (FEVD).  ... 
doi:10.1155/2019/8676531 fatcat:hz43iqb57vhdzpf7leqful6xea

Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model

Siem Jan Koopman, Rutger Lit, André Lucas
2017 Journal of the American Statistical Association  
The proposed methodology is applied to tick-by-tick data of four stocks traded on the New York Stock Exchange.  ...  An extensive forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches.  ...  The losses are based on the forecasting study presented in Section 3.7. The DM statistic represents the Table 3 3 shows that the forecasts based on Model C have always the lowest log loss.  ... 
doi:10.1080/01621459.2017.1302878 fatcat:357y75db35aoddxrwnud5ib5nu

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Guohui Li, Xiao Ma, Hong Yang
2018 Information  
For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce  ...  the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy.  ...  Lahmiri [25] proposed a hybrid model combining VMD and BP neural network for intraday stock price forecasting, and the results showed that this proposed model gave a good performance. Liang et al.  ... 
doi:10.3390/info9070177 fatcat:p7pv6aekffhw5oyj3z2itoeev4

Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting

Deyun Wang, Chenqiang Yue, Shuai Wei, Jun Lv
2017 Algorithms  
intrinsic time-scale decomposition (ITD) and variational mode decomposition (VMD).  ...  Initially, scholars focus on using single models based on only one linear or nonlinear forecasting method to forecast time series.  ...  Thus, based on decomposition techniques such as the wavelet transform (WT) family methods, the empirical mode decomposition (EMD) family approaches, and the variational mode decomposition (VMD) method,  ... 
doi:10.3390/a10030108 fatcat:2zmhksdegvdz5fmzehppxrur4y

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Chao Shi, Xiaosheng Zhuang
2019 Axioms  
The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices.  ...  Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong's market are undertaken  ...  Table 4 shows a brief summary of stock prediction by DWT. Empirical Mode Decomposition-Based Models Empirical mode decomposition (EMD) was invented by Huang et al.  ... 
doi:10.3390/axioms8040116 fatcat:rpafdfrm4ncadb7dj3qyp3aldq

Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

Yingchao Zou, Kaijian He
2022 Mathematics  
We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional  ...  The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting  ...  [25] identified the transient and extreme risk factors in the multiscale data domain extracted with the variational mode decomposition (VMD) model.  ... 
doi:10.3390/math10142413 fatcat:5zcgidrtxbe3dd5rnypyopxnam

Determinants of Commodity Futures Prices: Decomposition Approach

Emmanuel Antwi, Emmanuel N. Gyamfi, Kwabena Kyei, Ryan Gill, Anokye M. Adam, Yuxing Li
2021 Mathematical Problems in Engineering  
This study utilized the decomposition methods, empirical mode decomposition (EMD), and variational mode decomposition (VMD), to analyze three commodity futures prices data: corn from agricultural products  ...  In a nutshell, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary disequilibrium of supply-demand.  ...  For instance, Lahmiri [37] employed VMD as a data preprocessing tool to predict intraday stock prices at different frequencies. e study revealed that the VMD combined with other models forecast stock  ... 
doi:10.1155/2021/6032325 fatcat:3l7bkzreabfyhjpf5lfqqwkaoa

Improving Financial Time Series Prediction Accuracy using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks

Henry Chacon, Emre Kesici, Peyman Najafirad
2020 IEEE Access  
INDEX TERMS Empirical mode decomposition, EMD, artificial intelligence, machine learning, long short term memory, LSTM, sample entropy, stock price prediction, noise cancellation, time series forecasting  ...  forecast the next closing price.  ...  THE EMPIRICAL MODE DECOMPOSITION In asset pricing, it is common to find methods for explaining observed behaviors of stock prices based on related variables to induce causality.  ... 
doi:10.1109/access.2020.2996981 fatcat:x3s7vqzsvbg3bbrl6doutgb4he

Volatility Forecasting Based on Cyclical Two-Component Model: Evidence from Chinese Futures Markets and Sector Stocks

Conghua Wen, Junwei Wei
2020 Mathematical and Computational Applications  
An improved method based on the cyclical two-component model (CTCM) introduced by Harris et al. in 2011 is provided.  ...  This article aims to study the schemes of forecasting the volatilities of Chinese futures markets and sector stocks.  ...  This article aims to forecast the volatilities of Chinese futures markets and sector stocks based on cycling model.  ... 
doi:10.3390/mca25030059 fatcat:rtmzpap6cjfw3ecoiv7sql47ry

Modeling Liquidity Impact on Volatility: A GARCH-FunXL Approach

Andreas Fuest, Stefan Mittnik
2015 Social Science Research Network  
Applying our new methodology to intraday return data from the German stock exchange's XETRA system, we find a substantial liquidity impact on return variation.  ...  book (LOB) -on asset price volatility.  ...  Conversely, modes of variation that are rather unimportant for liquidity variation may well be of great importance for predicting price volatility.  ... 
doi:10.2139/ssrn.3038947 fatcat:rbo4f4j3mvffbhplm6k7fxjog4
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