Time Series Forecasting on Noisy Almost Periodic Functions (test) benchmark leaderboard · SOTA2 Research

Time Series Forecasting on Noisy Almost Periodic Functions (test)

0.922R2TCN

Evaluation Results

Method	Links
TCN2025.09		0.922	0.182
AR-KAN2025.09		0.913	0.191
AR-KAN2025.09		0.907	0.188
TCN2025.09		0.903	0.201
AR-KAN2025.09		0.895	0.208
TCN2025.09		0.888	0.221
ARIMA2025.09		0.868	0.217
FAN2025.09		0.822	0.274
ARIMA2025.09		0.782	0.28
Time-LLM2025.09		0.765	0.308
AR-KAN2025.09		0.76	0.364
Time-LLM2025.09		0.759	0.307
AR-KAN2025.09		0.749	0.368
TimesNet2025.09		0.738	0.308
TimesNet2025.09		0.725	0.329
AR-KAN2025.09		0.718	0.394
ARIMA2025.09		0.708	0.324
FAN2025.09		0.708	0.341
TCN2025.09		0.676	0.413
TCN2025.09		0.652	0.438
FAN2025.09		0.651	0.433
ARIMA2025.09		0.633	0.457
ARIMA2025.09		0.607	0.445
TCN2025.09		0.606	0.429
LSTM2025.09		0.601	0.397
Time-LLM2025.09		0.589	0.461
LSTM2025.09		0.575	0.388
ARIMA2025.09		0.567	0.457
LSTM2025.09		0.559	0.486
TimesNet2025.09		0.548	0.482
Time-LLM2025.09		0.538	0.477
TimesNet2025.09		0.506	0.493
Time-LLM2025.09		0.304	0.614
Time-LLM2025.09		0.29	0.663
FAN2025.09		0.276	0.592
TimesNet2025.09		0.154	0.654
TimesNet2025.09		0.147	0.74
LSTM2025.09		-0.008	0.704
FAN2025.09		-0.07	0.652
LSTM2025.09		-0.079	0.743
LSTM2025.09		-0.164	0.709
FAN2025.09		-0.719	0.965