পদ্ধতির তুলনা করুন

	SCINet: সময়-সিরিজ পূর্বাভাসের জন্য নমুনা কনভোলিউশন এবং ইন্টারঅ্যাকশন নেটওয়ার্ক	সময় সিরিজ পূর্বাভাসের জন্য DLinear: ডিকম্পোজিশন লিনিয়ার মডেল	TimesNet: সময় সিরিজের জন্য টেম্পোরাল ২ডি-ভ্যারিয়েশন মডেলিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2022	2023	2023
প্রবর্তক	Minhao Liu et al.	Ailing Zeng et al.	Haixu Wu et al.
ধরন	Hierarchical convolutional time-series forecasting network	Decomposition-based linear forecasting model	2D convolutional time-series model
মৌলিক উৎস	Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS.	Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI.	Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR.
অপর নাম	Sample Convolution and Interaction Network, SCI-Net, Temporal Downsampling Convolution Network, Örneklem Evrişim ve Etkileşim Ağı	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı
সম্পর্কিত	2	3	2
সারসংক্ষেপ	SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously.	DLinear is a lightweight time series forecasting model introduced by Zeng et al. at AAAI 2023. It challenges the prevailing assumption that Transformer-based architectures are necessary for accurate long-horizon forecasting. The model decomposes an input sequence into trend and seasonal components using a moving average filter, then applies separate single-layer linear transformations to each component before summing their outputs to produce the final forecast.	TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transform. This 1D-to-2D transformation exposes both intraperiod patterns (within one cycle) and interperiod trends (across cycles), enabling powerful 2D convolutional architectures to model temporal variation.