SVR-wavelet adaptive model for forecasting financial time series

Raimundo, Milton Saulo; Okamoto, Jun

doi:10.1109/infoct.2018.8356851

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…(1) EWT [15]- [17], [36], [37] is a novel data prepossessing model which has the formalism of WT and the adaptability of EMD [37], [38]. The prime advantage of EWT is to remedy the drawbacks that the primary WT requires specification of prior wavelet basis and parameters, as well as lack of self-adaptive method [39].…”

Section: Methodsmentioning

confidence: 99%

“…Taking the steady iterative state problem into account, an adaptive Elite-Opposition based algorithm referred to the inertia weight strategy of particle searching in the PSO algorithm [55] is presented in this study. The inertia weight W of artificial bees searching is calculating by (17):…”

Section: ) Elite Opposition Based Algorithmmentioning

confidence: 99%

“…The stock price series data decomposed by the WT, which made it more suitable for forecasting. Likewise, the studies of Wadia and Ismail [16] and Raimundo and Okamoto [17] applied the WT into ARIMA and SVM, respectively. Awajan et al [18] combined EMD with ARIMA to decompose the nonstationary and nonlinear stock market time-series data into Intrinsic Mode Functions (IMF) for ARIMA.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

Ming

Ruan

et al. 2020

IEEE Access

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The practical significances and complexities of financial time series analysis induce highly demand more reliable hybrid model that denoised the data efficiently, handled with both linear and nonlinear patterns in the data, to achieve more accurate results. This paper suggests a new forecasting hybrid model for financial time series data combined Empirical Wavelet Transform (EWT) technique with improved Artificial Bee Colony (ABC) algorithm, Extreme Learning Machine (ELM) neural network, and Auto-Regressive Integrated Moving Average (ARIMA) linear analysis algorithm. The EWT is used to decompose and denoise the data to reconstruct the data more suitable for forecast. The improvement of the ABC algorithm is according to the Good Point Sets (GPS) theory and adaptive Elite-based Opposition (EO) strategy (GPS-EO-ABC) to overcome the drawbacks of the original algorithm and enhance the optimization performance. The optimized ELM with GPS-EO-ABC, as well as the ARIMA, are utilized independently to generated different forecasting results and combined by the weight-based procession. We testify the performance of the proposed improved ABC algorithm by ten benchmark functions, simulating the proposed forecasting models by three financial time-series datasets. The results indicate that: (1) The proposed algorithm shows outstanding capacities in parameter optimization. The optimized ELM generated more stable and precise results compared with original ELM, ABC-ELM, single LSTM, and ANN; (2) The proposed hybrid model has not only effectiveness but also efficiencies in denoising data, correcting outliers, coordinating both linear and nonlinear patterns, its performance in financial time series forecasting is more excellent than existing hybrid models.INDEX TERMS Financial time-series forecasting, EWT data prepossessing, ARIMA model, GPS-EO-ABC ELM model, hybrid model methodology.

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Section: Methodsmentioning

confidence: 99%

Section: ) Elite Opposition Based Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

Ming

Ruan

et al. 2020

IEEE Access

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“…Milton and Jun [48] proposed a hybrid model for FOREX prediction that uses wavelet models along with support vector regression (SVR). At first, they used a discrete wavelet transform (DWT) method to interpret information from their FOREX dataset.…”

Section: Regression Methodsmentioning

confidence: 99%

“…However, there are other algorithms such as lasso regression, logistic regression, and multivariate regression which were not explored and can be used for forecasting in later years. The reviewed literature suggests that the system based on a regression model performed better than ARIMA and ARFIMA models [48], and the model performance may increase to a great extent [41] when a regression model is combined with other techniques. However, when working with a large number of currency pairs, it may become difficult with regression techniques, as most of the currency pairs return a higher MSE [49].…”

Section: Regression Methodsmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

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In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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