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| ARIMA (Autoregressive Integrated Moving Average) 모형× | TSMixer: 시계열 예측을 위한 완전 MLP 아키텍처× | |
|---|---|---|
| 분야≠ | 계량경제학 | 딥러닝 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 2015 | 2023 |
| 창시자≠ | Box & Jenkins (Box-Jenkins methodology) | Si-An Chen et al. (Google) |
| 유형≠ | Univariate time-series model | All-MLP multivariate time-series forecasting model |
| 원전≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research. link ↗ |
| 별칭≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı |
| 관련≠ | 5 | 3 |
| 요약≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally. |
| ScholarGate데이터셋 ↗ |
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