পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Autoformer: দীর্ঘমেয়াদী সময়-সিরিজ পূর্বাভাসের জন্য ডিকম্পোজিশন ট্রান্সফরমার× | স্টেট স্পেস মডেল (কালম্যান ফিল্টার)× | |
|---|---|---|
| ক্ষেত্র≠ | গভীর শিখন | অর্থমিতি |
| পরিবার≠ | Machine learning | Regression model |
| উদ্ভবের বছর≠ | 2021 | 1990 |
| প্রবর্তক≠ | Haixu Wu et al. (Tsinghua) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| ধরন≠ | Decomposition-based deep forecasting model | State space time series model |
| মৌলিক উৎস≠ | Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34. link ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| অপর নাম | Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components. | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. |
| ScholarGateডেটাসেট ↗ |
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