Matches in SemOpenAlex for { <https://semopenalex.org/work/W3005981864> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3005981864 endingPage "218" @default.
- W3005981864 startingPage "197" @default.
- W3005981864 abstract "This chapter compares the performances of multiple Big Data techniques applied for time series forecasting and traditional time series models on three Big Data sets. The traditional time series models, Autoregressive Integrated Moving Average (ARIMA), and exponential smoothing models are used as the baseline models against Big Data analysis methods in the machine learning. These Big Data techniques include regression trees, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), and long short-term memory neural networks (LSTM). Across three time series data sets used (unemployment rate, bike rentals, and transportation), this study finds that LSTM neural networks performed the best. In conclusion, this study points out that Big Data machine learning algorithms applied in time series can outperform traditional time series models. The computations in this work are done by Python, one of the most popular open-sourced platforms for data science and Big Data analysis." @default.
- W3005981864 created "2020-02-24" @default.
- W3005981864 creator A5073735921 @default.
- W3005981864 creator A5086764406 @default.
- W3005981864 date "2020-01-01" @default.
- W3005981864 modified "2023-10-18" @default.
- W3005981864 title "A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python" @default.
- W3005981864 cites W1547333707 @default.
- W3005981864 cites W1982348007 @default.
- W3005981864 cites W2021912245 @default.
- W3005981864 cites W2029803196 @default.
- W3005981864 cites W2032717371 @default.
- W3005981864 cites W2040395995 @default.
- W3005981864 cites W2088621366 @default.
- W3005981864 cites W2111991989 @default.
- W3005981864 cites W2117014758 @default.
- W3005981864 cites W2131819535 @default.
- W3005981864 cites W2915091860 @default.
- W3005981864 doi "https://doi.org/10.4018/978-1-7998-2768-9.ch007" @default.
- W3005981864 hasPublicationYear "2020" @default.
- W3005981864 type Work @default.
- W3005981864 sameAs 3005981864 @default.
- W3005981864 citedByCount "0" @default.
- W3005981864 crossrefType "book-chapter" @default.
- W3005981864 hasAuthorship W3005981864A5073735921 @default.
- W3005981864 hasAuthorship W3005981864A5086764406 @default.
- W3005981864 hasConcept C111919701 @default.
- W3005981864 hasConcept C11413529 @default.
- W3005981864 hasConcept C119857082 @default.
- W3005981864 hasConcept C12267149 @default.
- W3005981864 hasConcept C124101348 @default.
- W3005981864 hasConcept C133710760 @default.
- W3005981864 hasConcept C143724316 @default.
- W3005981864 hasConcept C151406439 @default.
- W3005981864 hasConcept C151730666 @default.
- W3005981864 hasConcept C154945302 @default.
- W3005981864 hasConcept C24338571 @default.
- W3005981864 hasConcept C31972630 @default.
- W3005981864 hasConcept C41008148 @default.
- W3005981864 hasConcept C50644808 @default.
- W3005981864 hasConcept C519991488 @default.
- W3005981864 hasConcept C60908668 @default.
- W3005981864 hasConcept C75684735 @default.
- W3005981864 hasConcept C86803240 @default.
- W3005981864 hasConceptScore W3005981864C111919701 @default.
- W3005981864 hasConceptScore W3005981864C11413529 @default.
- W3005981864 hasConceptScore W3005981864C119857082 @default.
- W3005981864 hasConceptScore W3005981864C12267149 @default.
- W3005981864 hasConceptScore W3005981864C124101348 @default.
- W3005981864 hasConceptScore W3005981864C133710760 @default.
- W3005981864 hasConceptScore W3005981864C143724316 @default.
- W3005981864 hasConceptScore W3005981864C151406439 @default.
- W3005981864 hasConceptScore W3005981864C151730666 @default.
- W3005981864 hasConceptScore W3005981864C154945302 @default.
- W3005981864 hasConceptScore W3005981864C24338571 @default.
- W3005981864 hasConceptScore W3005981864C31972630 @default.
- W3005981864 hasConceptScore W3005981864C41008148 @default.
- W3005981864 hasConceptScore W3005981864C50644808 @default.
- W3005981864 hasConceptScore W3005981864C519991488 @default.
- W3005981864 hasConceptScore W3005981864C60908668 @default.
- W3005981864 hasConceptScore W3005981864C75684735 @default.
- W3005981864 hasConceptScore W3005981864C86803240 @default.
- W3005981864 hasLocation W30059818641 @default.
- W3005981864 hasOpenAccess W3005981864 @default.
- W3005981864 hasPrimaryLocation W30059818641 @default.
- W3005981864 hasRelatedWork W1975230757 @default.
- W3005981864 hasRelatedWork W2100115021 @default.
- W3005981864 hasRelatedWork W2365520989 @default.
- W3005981864 hasRelatedWork W2979979539 @default.
- W3005981864 hasRelatedWork W3005981864 @default.
- W3005981864 hasRelatedWork W3116240832 @default.
- W3005981864 hasRelatedWork W3202971245 @default.
- W3005981864 hasRelatedWork W4286908664 @default.
- W3005981864 hasRelatedWork W4310009592 @default.
- W3005981864 hasRelatedWork W4361795583 @default.
- W3005981864 isParatext "false" @default.
- W3005981864 isRetracted "false" @default.
- W3005981864 magId "3005981864" @default.
- W3005981864 workType "book-chapter" @default.