Time Series Forecasting On Etth2 96 1

Métriques

MSE

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
DLinear	0.289	Are Transformers Effective for Time Series Forecasting?
TEFN	0.288	Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
MoLE-DLinear	0.287	Mixture-of-Linear-Experts for Long-term Time Series Forecasting
FiLM	0.284	FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
NLinear	0.277	Are Transformers Effective for Time Series Forecasting?
TSMixer	0.276	TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
DiPE-Linear	0.275	Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting
PatchTST/64	0.274	A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
MoLE-RLinear	0.273	Mixture-of-Linear-Experts for Long-term Time Series Forecasting
TiDE	0.27	Long-term Forecasting with TiDE: Time-series Dense Encoder
PRformer	0.268	PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting
LTBoost (drop_last=false)	0.263	LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting
SegRNN	0.263	SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
RLinear	0.262	Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
xPatch	0.226	xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition
PatchMixer	0.225	PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting

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Time Series Forecasting On Etth2 96 1 | SOTA | HyperAI