পদ্ধতির তুলনা করুন

	টেম্পোরাল ফিউশন ট্রান্সফর্মার	ARIMA (Autoregressive Integrated Moving Average) মডেল
ক্ষেত্র	গভীর শিখন	অর্থমিতি
পরিবার	Machine learning	Regression model
উদ্ভবের বছর	2021	2015
প্রবর্তক	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.	Box & Jenkins (Box-Jenkins methodology)
ধরন	Attention-based deep learning forecasting architecture	Univariate time-series model
মৌলিক উৎস	Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764.	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
অপর নাম	Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
সম্পর্কিত	6	5
সারসংক্ষেপ	The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts.	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).