Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Chronos× | LSTM× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2024 | 1997 |
| Автор метода≠ | Abdul Fatir Ansari et al. (Amazon) | Hochreiter, S. & Schmidhuber, J. |
| Тип≠ | Pre-trained language-model-based time-series forecaster | Recurrent neural network (gated memory cell) |
| Основополагающий источник≠ | Ansari, A. F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., et al. (2024). Chronos: Learning the language of time series. Transactions on Machine Learning Research. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Другие названия | Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells |
| Связанные≠ | 2 | 5 |
| Сводка≠ | Chronos is a family of pre-trained probabilistic forecasting models introduced by Ansari et al. at Amazon in 2024. It adapts the language-model paradigm to time series by quantizing continuous values into discrete tokens, enabling a standard transformer to be trained on a large heterogeneous corpus of time-series data. The result is a zero-shot forecasting model that generalizes across domains without requiring dataset-specific retraining. | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. |
| ScholarGateНабор данных ↗ |
|
|