পদ্ধতির তুলনা করুন

	FreTS: ফ্রিকোয়েন্সি-ডোমেইন এমএলপি (MLP) ব্যবহার করে টাইম সিরিজ পূর্বাভাস	FEDformer: ফ্রিকোয়েন্সি এনহ্যান্সড ডিকম্পোজড ট্রান্সফরমার	FiLM: ফ্রিকোয়েন্সি উন্নত Legendre মেমরি মডেল	TSMixer: সময় সিরিজের পূর্বাভাসের জন্য সম্পূর্ণ MLP আর্কিটেকচার
ক্ষেত্র	গভীর শিখন	গভীর শিখন	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2023	2022	2022	2023
প্রবর্তক	Kun Yi et al.	Tian Zhou et al.	Tian Zhou et al.	Si-An Chen et al. (Google)
ধরন	Frequency-domain MLP forecasting model	Frequency-domain decomposed Transformer for time-series forecasting	Frequency-domain time-series forecasting model	All-MLP multivariate time-series forecasting model
মৌলিক উৎস	Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., An, N., Lian, D., Cao, L., & Niu, Z. (2023). Frequency-domain MLPs are more effective learners in time series forecasting. NeurIPS.	Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. ICML.	Zhou, T., Ma, Z., Wen, Q., Sun, L., Yao, T., Yin, W., & Jin, R. (2022). FiLM: Frequency improved Legendre memory model for long-term time series forecasting. NeurIPS.	Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research.
অপর নাম	Frequency-domain MLPs, FrequencyMLP, FreTS Forecaster, Frekans Alanı MLP	Frequency Enhanced Decomposed Transformer, FED-Transformer, Frequency Domain Transformer, Frekans Tabanlı Ayrıştırılmış Dönüştürücü	Frequency Improved Legendre Memory, FiLM Forecaster, Legendre Frequency Model, Frekans Tabanlı Legendre Bellek Modeli	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı
সম্পর্কিত	3	3	3	3
সারসংক্ষেপ	FreTS is a time series forecasting architecture introduced by Yi et al. at NeurIPS 2023. It departs from Transformer-based designs by applying simple Multi-Layer Perceptrons (MLPs) entirely in the frequency domain. The model transforms input sequences with the Discrete Fourier Transform and then learns temporal and channel dependencies through complex-valued MLP layers, achieving competitive or superior long-term forecasting accuracy with substantially lower computational cost.	FEDformer is a Transformer-based architecture for long-term multivariate time-series forecasting, introduced by Zhou et al. at ICML 2022. Its core innovation is the combination of seasonal-trend decomposition with frequency-domain attention: instead of computing full token-to-token attention in the time domain, FEDformer projects queries, keys, and values into the frequency domain via Fourier or wavelet transforms and operates on a randomly selected subset of frequency components, achieving linear complexity while preserving global temporal structure.	FiLM is a long-term time-series forecasting architecture introduced by Tian Zhou and colleagues at NeurIPS 2022. It combines Legendre polynomial projections of the historical input with learnable frequency-domain filters applied to the resulting coefficient sequences. By representing history as a compact set of polynomial coefficients and filtering those coefficients in the frequency domain, FiLM enables efficient extrapolation over long prediction horizons without the quadratic cost of full self-attention.	TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally.