Matches in SemOpenAlex for { <https://semopenalex.org/work/W3207629440> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W3207629440 endingPage "4845" @default.
- W3207629440 startingPage "4829" @default.
- W3207629440 abstract "Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient forecasting accuracy. The present study proposes a hybrid forecasting methods to address this need. The proposed method includes three models. The first model is based on the autoregressive integrated moving average (ARIMA) statistical model; the second model is a back propagation neural network (BPNN) with adaptive slope and momentum parameters; and the third model is a hybridization between ARIMA and BPNN (ARIMA/BPNN) and artificial neural networks and ARIMA (ARIMA/ANN) to gain the benefits of linear and nonlinear modeling. The forecasting models proposed in this study are used to predict the indices of the consumer price index (CPI), and predict the expected number of cancer patients in the Ibb Province in Yemen. Statistical standard measures used to evaluate the proposed method include (i) mean square error, (ii) mean absolute error, (iii) root mean square error, and (iv) mean absolute percentage error. Based on the computational results, the improvement rate of forecasting the CPI dataset was 5%, 71%, and 4% for ARIMA/BPNN model, ARIMA/ANN model, and BPNN model respectively; while the result for cancer patients’ dataset was 7%, 200%, and 19% for ARIMA/BPNN model, ARIMA/ANN model, and BPNN model respectively. Therefore, it is obvious that the proposed method reduced the randomness degree, and the alterations affected the time series with data non-linearity. The ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting." @default.
- W3207629440 created "2021-10-25" @default.
- W3207629440 creator A5001599355 @default.
- W3207629440 creator A5029615544 @default.
- W3207629440 creator A5035218708 @default.
- W3207629440 creator A5039401755 @default.
- W3207629440 creator A5066689489 @default.
- W3207629440 date "2022-01-01" @default.
- W3207629440 modified "2023-10-14" @default.
- W3207629440 title "A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting" @default.
- W3207629440 cites W1984755515 @default.
- W3207629440 cites W1986078433 @default.
- W3207629440 cites W1989130706 @default.
- W3207629440 cites W2002044606 @default.
- W3207629440 cites W2010538021 @default.
- W3207629440 cites W2019960288 @default.
- W3207629440 cites W2038781693 @default.
- W3207629440 cites W2051272931 @default.
- W3207629440 cites W2059804518 @default.
- W3207629440 cites W2091998246 @default.
- W3207629440 cites W2100982489 @default.
- W3207629440 cites W2149905014 @default.
- W3207629440 cites W2343238411 @default.
- W3207629440 cites W2401614582 @default.
- W3207629440 cites W2757248671 @default.
- W3207629440 cites W3044995911 @default.
- W3207629440 cites W385004530 @default.
- W3207629440 cites W965752054 @default.
- W3207629440 doi "https://doi.org/10.32604/cmc.2022.017824" @default.
- W3207629440 hasPublicationYear "2022" @default.
- W3207629440 type Work @default.
- W3207629440 sameAs 3207629440 @default.
- W3207629440 citedByCount "5" @default.
- W3207629440 countsByYear W32076294402022 @default.
- W3207629440 countsByYear W32076294402023 @default.
- W3207629440 crossrefType "journal-article" @default.
- W3207629440 hasAuthorship W3207629440A5001599355 @default.
- W3207629440 hasAuthorship W3207629440A5029615544 @default.
- W3207629440 hasAuthorship W3207629440A5035218708 @default.
- W3207629440 hasAuthorship W3207629440A5039401755 @default.
- W3207629440 hasAuthorship W3207629440A5066689489 @default.
- W3207629440 hasBestOaLocation W32076294401 @default.
- W3207629440 hasConcept C105795698 @default.
- W3207629440 hasConcept C139945424 @default.
- W3207629440 hasConcept C143724316 @default.
- W3207629440 hasConcept C149782125 @default.
- W3207629440 hasConcept C150217764 @default.
- W3207629440 hasConcept C151406439 @default.
- W3207629440 hasConcept C151730666 @default.
- W3207629440 hasConcept C154945302 @default.
- W3207629440 hasConcept C175706884 @default.
- W3207629440 hasConcept C24338571 @default.
- W3207629440 hasConcept C33923547 @default.
- W3207629440 hasConcept C41008148 @default.
- W3207629440 hasConcept C50644808 @default.
- W3207629440 hasConcept C82257358 @default.
- W3207629440 hasConcept C86803240 @default.
- W3207629440 hasConceptScore W3207629440C105795698 @default.
- W3207629440 hasConceptScore W3207629440C139945424 @default.
- W3207629440 hasConceptScore W3207629440C143724316 @default.
- W3207629440 hasConceptScore W3207629440C149782125 @default.
- W3207629440 hasConceptScore W3207629440C150217764 @default.
- W3207629440 hasConceptScore W3207629440C151406439 @default.
- W3207629440 hasConceptScore W3207629440C151730666 @default.
- W3207629440 hasConceptScore W3207629440C154945302 @default.
- W3207629440 hasConceptScore W3207629440C175706884 @default.
- W3207629440 hasConceptScore W3207629440C24338571 @default.
- W3207629440 hasConceptScore W3207629440C33923547 @default.
- W3207629440 hasConceptScore W3207629440C41008148 @default.
- W3207629440 hasConceptScore W3207629440C50644808 @default.
- W3207629440 hasConceptScore W3207629440C82257358 @default.
- W3207629440 hasConceptScore W3207629440C86803240 @default.
- W3207629440 hasIssue "3" @default.
- W3207629440 hasLocation W32076294401 @default.
- W3207629440 hasOpenAccess W3207629440 @default.
- W3207629440 hasPrimaryLocation W32076294401 @default.
- W3207629440 hasRelatedWork W2057618554 @default.
- W3207629440 hasRelatedWork W2305568609 @default.
- W3207629440 hasRelatedWork W2778123278 @default.
- W3207629440 hasRelatedWork W2906471315 @default.
- W3207629440 hasRelatedWork W2936756612 @default.
- W3207629440 hasRelatedWork W2948406996 @default.
- W3207629440 hasRelatedWork W3037634211 @default.
- W3207629440 hasRelatedWork W3082735596 @default.
- W3207629440 hasRelatedWork W3180276677 @default.
- W3207629440 hasRelatedWork W3185524054 @default.
- W3207629440 hasVolume "70" @default.
- W3207629440 isParatext "false" @default.
- W3207629440 isRetracted "false" @default.
- W3207629440 magId "3207629440" @default.
- W3207629440 workType "article" @default.