Comparison of predictive models for forecasting timeseries data

Date

2019-11-20

Author

Özen, Serkan
Atalay, Mehmet Volkan
Yazıcı, Adnan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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© 2019 Association for Computing Machinery.Dramatic increase in data size enabled researchers to study analysis and prediction of big data. Big data can be formed in many ways and one alternative is through the use of sensors. An important aspect of data coming from sensors is that they are time-series data. Although forecasting based on time-series data has been studied widely, it is still possible to advance the state-ofthe- art by constructing new hybrid deep learning models. In this study, Random Forest, Convolutional Neural Network, Long Short Term Memory and hybrid Convolutional Neural Network- Long Short Term Memory models are applied and assessed on meteorological time-series data. Vector Auto-regression model and Multi-layer Perceptron model are used as the baseline forecasting methods for comparison purposes. Root Mean Square Error of the models for predictions are calculated for performance assessment which reveals the performance of these deep learning methods for forecasting based on time-series data.

URI

https://hdl.handle.net/11511/56420

DOI

https://doi.org/10.1145/3372454.3372482

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. Özen, M. V. Atalay, and A. Yazıcı, “Comparison of predictive models for forecasting timeseries data,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56420.