DeepAR
DeepAR ni mfumo wa utabiri wa kiviwanda wa Amazon, ulioanzishwa na Salinas, Flunkert na Gasthaus (2017; ulichapishwa 2020), unaotumia mtandao wa neva unaojirudia wa kujiendesha (autoregressive recurrent neural network) kukadiria vigezo vya usambazaji wa uwezekano katika kila hatua, ukitoa kipindi cha kujiamini badala ya utabiri wa uhakika mmoja. Unaweza kuiga mfululizo mwingi wa data za muda zinazohusiana kwa pamoja ndani ya mfumo mmoja.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191. DOI: 10.1016/j.ijforecast.2019.07.001 ↗
- Salinas, D., Flunkert, V. & Gasthaus, J. (2017). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. arXiv:1704.04110. link ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 1). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. ScholarGate. https://scholargate.app/sw/deep-learning/deepar
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Mfumo wa ARIMA (Autoregressive Integrated Moving Average)Ekonometriki↔ compare
- Utabiri Konformali kwa Utabiri wa Mfululizo wa WakatiEkonometriki↔ compare
- N-HiTSUjifunzaji wa Kina↔ compare
- PatchTSTUjifunzaji wa Kina↔ compare
- Msitu NasibuUjifunzaji wa Mashine↔ compare
Imerejelewa na
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