English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Many applications in different domains produce large amount of time seriesdata. Making accurate forecasting is critical for many decision makers. Varioustime series forecasting methods exist which use linear and nonlinear modelsseparately or combination of both. Studies show that combining of linear andnonlinear models can be effective to improve forecasting performance. However,some assumptions that those existing methods make, might restrict theirperformance in certain situations. We provide a new Autoregressive IntegratedMoving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that workin a more general framework. Experimental results show that strategies fordecomposing the original data and for combining linear and nonlinear modelsthroughout the hybridization process are key factors in the forecastingperformance of the methods. By using appropriate strategies, our hybrid methodcan be an effective way to improve forecasting accuracy obtained by traditionalhybrid methods and also either of the individual methods used separately.

Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition