Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

Lu, Junjie; Huang, Jinquan; Lü, Feng

doi:10.3390/app7030217

Cited by 10 publications

(12 citation statements)

References 26 publications

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“…ANN Characterized by some qualities that assist them in reaching the distinctive solutions through its applications in the areas of purpose to identify the linear and nonlinear models [13]. Fig.…”

Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

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Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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Abstract-Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study is to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated, namely, ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model is examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

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Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

“…Recently, intelligence models of ANN known for its propensity to identify the non-linear characteristics present within the time series data specially in FTS forecasting [7], [13]. ANN applied in multiple layered to predict exchange rate which reached sensible results.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

View full text Add to dashboard Cite

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“…However, in many practical applications, such as the real-time radio-frequency identification (RFID) indoor positioning system for shop-floor management (Yang et al, 2015) and time series prediction (Lu et al, 2017), data are acquired sequentially, and CELM has to retrain all of the CELM processes to account for the new data. This causes a large amount of unnecessary training time.…”

Section: Discussionmentioning

confidence: 99%

Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine

Atsawaraungsuk¹,

Katanyukul²

2017

IJTech

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This article discusses the development of the online sequential circular extreme learning machine (OS-CELM) and structural tolerance OS-CELM (STOS-CELM). OS-CELM is developed based on the circular extreme learning machine (CELM) to enable sequential learning. It can update a new chunk of data by spending less training time to update the chunk than the batch CELM. STOS-CELM is developed based on an idea similar to that of OS-CELM, but with a Householder block exact inverse QR decomposition (QRD) recursive least squares (QRD-RLS) algorithm to allow sequential learning and mitigate the criticality of deciding the number of hidden nodes. In addition, our experiments have shown that given the same hidden node setting, STOS-CELM can deliver accuracy comparable to a batch CELM approach and also has higher accuracy than the original online sequential extreme learning machine (OS-ELM) and structural tolerance OS-ELM (STOS-ELM) in classification problems, especially those involving high dimension datasets.

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“…Note that in equation (28), the calculation of b k depends on e k , but the e k is unacquainted when the b k is unknown. That is, there is an interdependence between b k and e k .…”

Section: Recursive Solution Of the Ra-oselm Modelmentioning

confidence: 99%

Robust adaptive online sequential extreme learning machine for predicting nonstationary data streams with outliers

Wei

2019

Journal of Algorithms & Computational Technology

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Data streams online modeling and prediction is an important research direction in the field of data mining. In practical applications, data streams are often of nonstationary nature and containing outliers, hence an online learning algorithm with dynamic tracking capability as well as anti-outlier capability is urgently needed. With this in mind, this paper proposes a novel robust adaptive online sequential extreme learning machine (RA-OSELM) algorithm for the online modeling and prediction of nonstationary data streams with outliers. The RA-OSELM is developed from the famous online sequential extreme learning machine algorithm, but it uses a more robust M-estimation loss function to replace the conventional least square loss function so as to suppress the incorrect online update of the learning algorithm with respect to outliers, and hence enhances its robustness in the presence of outliers. Moreover, the RA-OSELM adopts a variable forgetting factor method to automatically track the dynamic changes of the nonstationary data streams and timely eliminate the negative impacts of the outdated data, so it tends to produce satisfying tracking results in nonstationary environments. The performances of RA-OSELM are evaluated and compared with other representative algorithms with synthetic and real data sets, and the experimental results indicate that the proposed algorithm has better adaptive tracking capability with stronger robustness than its counterparts for predicting nonstationary data streams with outliers.

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Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

Cited by 10 publications

References 26 publications

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine

Robust adaptive online sequential extreme learning machine for predicting nonstationary data streams with outliers

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