پیش بینی سری زمانی با مجموعه داده های ناقص
ترجمه نشده

پیش بینی سری زمانی با مجموعه داده های ناقص

عنوان فارسی مقاله: پیش بینی سری زمانی با مجموعه داده های ناقص بر اساس شبکه حالت پژواکی عمیق دو طرفه
عنوان انگلیسی مقاله: Time Series Prediction With Incomplete Dataset Based on Deep Bidirectional Echo State Network
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری
کلمات کلیدی فارسی: یادگیری عمیق، شبکه حالت پژواکی، مجموعه داده های ناقص، پیش بینی، سری زمانی
کلمات کلیدی انگلیسی: Deep learning, echo state network, incomplete dataset, prediction, time series
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2948367
دانشگاه: School of Control Sciences and Engineering, Dalian University of Technology, Dalian 116023, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13900
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Related Works and Preliminaries


III. Deep Bidirectional Echo State Network Framework-Based Time Series Prediction


IV. Experiments and Analysis


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


In the complex industrial environment, data missing situation is often occurred in the process of data acquisition and transition. The major contribution of the paper is the proposal of a deep bidirectional echo state network (DBESN) framework for time series prediction with such incomplete dataset. Instead of data imputation methodology, a bidirectional fusion reservoir is here designed to extract the deep bidirectional feature along with forward and backward time scales, based on which a deep autoencoder echo state network (DAESN) and a deep bidirectional state echo state network (DBSESN) are constructed for the incomplete output and input samples, respectively. As for such two networks, a bidirectional echo state network (BESN) is proposed for connecting them to constitute the DBESN framework for prediction. To verify the effectiveness of the proposed method, one synthetic time series as well as two realworld industrial datasets are employed to conduct the comparative experiments. The experimental results demonstrate that the proposed method outperforms other comparative ones at various missing rates.


Introduction


With the development of modern industrial information technology, the amount of the industrial data accumulated in process of manufacturing is increasing at an unprecedented rate [1]. To monitor and analyze the state of energy utilization in industrial process, it is necessary to establish a prediction model for some crucial variables based on these process data [2]. Since, in the process of industrial data acquisition and transition, due to the collector faults, transmission errors, memory failures and human errors, the data absence phenomenon may occur, which often brings a huge challenge for data analysis and processing [3]. Furthermore, the existence of missing data may largely increase the difficulties to describe a system by using data-driven approaches [4]. Therefore, it becomes essential to construct a prediction model for the incomplete data to provide a significative guidance for energy scheduling to avoid energy waste in the industrial process. In literature, a series of researches exist on the time series prediction with incomplete dataset in recent years, but most of them only considered the issue of data imputation [5]. For example, a multiple imputation using Markov Chain Monte Carlo (MCMC) algorithm were adopted in [6] to impute the missing data by using partial least squares regression model. Besides, periodicity imputation method, mean imputation method and cubic spline imputation method were employed in [7] for incomplete time series data imputation before modeling, in which the differences between them were analyzed. Furthermore, a polyfit line-fitting algorithm [8], a Gaussian process [9] and a nearest neighbor method [10] were also applied to data imputation. However, one of the major drawbacks of data imputation lies in that the original feature of the data may be retorted by imputation, which could exhibit an adverse effect on the prediction accuracy.

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