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Conference Paper
A Study on Online ARIMA Algorithms Applying Various Gradient Descent Optimization Algorithms for Time Series Prediction
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- Authors
- Jungi Lee, HyunYong Lee, NacWoo Kim, ByungTak Lee
- Issue Date
- 2021-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.1104-1106
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- The prediction of time series data can be used in various fields, and many research for this is required. This paper has researched about applying various optimizers for online ARIMA model, which is used to make a prediction on time series data. For this work, algorithms for gradient descent like Adam, was used for updating online ARIMA model's weight for predicting the values. Our approach to finding a gradient descent algorithm that can obtain higher performance in time series prediction, the performance of the online ARIMA-based model was evaluated with the photovoltaic power generation data and gold price data. As a result, it was confirmed that there is a gradient descent-based optimization algorithm that improves the prediction performance for each time-series data.
- KSP Keywords
- ARIMA model, Gradient Descent Optimization, Gradient descent algorithm, Higher performance, Optimization algorithm, Time series data, gold price, photovoltaic power generation, prediction performance, time series prediction