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Time Series Forecasting On Etth1 720 2

Metrics

MAE

MSE

Results

Performance results of various models on this benchmark

Model Name	MAE	MSE	Paper Title	Repository
DLinear	0.359	0.189	Are Transformers Effective for Time Series Forecasting?	-
Transformer	0.4213	0.2501	Long-term series forecasting with Query Selector -- efficient model of sparse attention	-
QuerySelector	0.373	0.2136	Long-term series forecasting with Query Selector -- efficient model of sparse attention	-
Informer	0.357	0.201	Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting	-
SCINet	0.25	0.099	SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction	-
PatchTST/64	0.236	0.087	A Time Series is Worth 64 Words: Long-term Forecasting with Transformers	-
FiLM	0.24	0.09	FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting	-
AutoCon	0.223	0.078	Self-Supervised Contrastive Learning for Long-term Forecasting	-
SegRNN	0.233	0.085	SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting	-
PatchMixer	0.243	0.093	PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting	-
NLinear	0.226	0.08	Are Transformers Effective for Time Series Forecasting?	-
Parallel Series Transformer	0.286	0.129	How Features Benefit: Parallel Series Embedding for Multivariate Time Series Forecasting with Transformer

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Time Series Forecasting On Etth1 720 2 | SOTA | HyperAI