کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11002350 | 1437946 | 2018 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel spark-based multi-step forecasting algorithm for big data time series
ترجمه فارسی عنوان
یک الگوریتم پیش بینی چند مرحله ای مبتنی بر جرقه برای سری زمانی داده های بزرگ
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کلمات کلیدی
اطلاعات بزرگ، مقیاس پذیر، سری زمان برق پیش بینی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper presents different scalable methods for predicting big time series, namely time series with a high frequency measurement. Methods are also developed to deal with arbitrary prediction horizons. The Apache Spark framework is proposed for distributed computing in order to achieve the scalability of the methods. Prediction methods have been developed using Spark's MLlib library for machine learning. Since the library does not support multivariate regression, the prediction problem is formulated as h prediction sub-problems, where h is the number of future values to predict, that is, the prediction horizon. Furthermore, different kinds of representative methods have been chosen, such as decision trees, two tree-based ensemble techniques (Gradient-Boosted and Random Forest) and a linear regression method as a reference method for comparisons. Finally, the methodology has been tested in a real time series of electrical demand in Spain, with a time interval of ten minutes between measurements.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 467, October 2018, Pages 800-818
Journal: Information Sciences - Volume 467, October 2018, Pages 800-818
نویسندگان
A. Galicia, J.F. Torres, F. MartÃnez-Álvarez, A. Troncoso,