Coaster41/patchtst_tsmixup_final

patchtst-tsmixup

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1553
• Mse: 280.0361
• Mae: 0.6489
• Rmse: 16.7343
• Smape: 100.3318

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 256
• eval_batch_size: 512
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 512
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Mae	Rmse	Smape
0.1797	0.0952	1000	0.1756	447.3596	0.7397	21.1509	90.8971
0.1709	0.1904	2000	0.1691	425.0924	0.7153	20.6178	112.3049
0.1722	0.2857	3000	0.1662	516.2153	0.7009	22.7204	89.5236
0.1694	0.3809	4000	0.1643	321.2047	0.6708	17.9222	93.0515
0.1648	0.4761	5000	0.1626	350.6870	0.6731	18.7266	94.0748
0.1672	0.5713	6000	0.1612	370.8825	0.6797	19.2583	84.6619
0.1623	0.6666	7000	0.1605	400.0790	0.6715	20.0020	89.7598
0.1638	0.7618	8000	0.1613	387.6971	0.6771	19.6900	122.3799
0.1609	0.8570	9000	0.1602	335.3427	0.6603	18.3124	109.3877
0.1618	0.9522	10000	0.1592	318.1492	0.6688	17.8367	76.3322
0.1588	1.0474	11000	0.1586	345.3675	0.6628	18.5841	94.5032
0.1601	1.1426	12000	0.1580	326.8865	0.6540	18.0800	81.2504
0.1585	1.2379	13000	0.1575	279.7964	0.6532	16.7271	107.6181
0.1567	1.3331	14000	0.1575	328.3490	0.6622	18.1204	91.9899
0.1592	1.4283	15000	0.1567	376.8973	0.6523	19.4138	89.7952
0.16	1.5235	16000	0.1576	327.5271	0.6580	18.0977	105.7316
0.1586	1.6188	17000	0.1568	399.5775	0.6602	19.9894	88.6057
0.1593	1.7140	18000	0.1565	359.5630	0.6604	18.9621	325.5064
0.1562	1.8092	19000	0.1566	281.2739	0.6545	16.7712	80.4528
0.1601	1.9044	20000	0.1570	287.3577	0.6543	16.9516	79.5544
0.1551	1.9997	21000	0.1561	279.2150	0.6444	16.7097	102.6016
0.1532	2.0948	22000	0.1554	282.9574	0.6454	16.8213	85.0121
0.1564	2.1901	23000	0.1554	332.3758	0.6485	18.2312	76.0350
0.1568	2.2853	24000	0.1551	356.0441	0.6528	18.8691	92.2597
0.1569	2.3805	25000	0.1562	333.3135	0.6536	18.2569	180.8556
0.1569	2.4757	26000	0.1551	291.0384	0.6491	17.0598	80.7309
0.1532	2.5710	27000	0.1553	280.0361	0.6489	16.7343	100.3318

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.1+cu126
• Datasets 2.17.1
• Tokenizers 0.21.1