Comparison of Predictive Models for Forecasting Time-series Data
| Autor: | Adnan Yazici, Volkan Atalay, Serkan Özen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Mean squared error business.industry Computer science Deep learning 05 social sciences Big data Supervised learning 02 engineering and technology Machine learning computer.software_genre Perceptron Convolutional neural network Random forest 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Time series business computer |
| Zdroj: | ICBDR |
| Popis: | 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-of-the-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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|