如何使用这个模型推理

[tianlianghai]

已经在Bike数据集训练和微调好了，使用自己的数据来进行预测呢

[YuanYuan98]

Please format your own data according to the guidelines provided in our dataset documentation.

[tianlianghai]

thanks for your reply, but I looked up on the internet and the paper. I don't find how the data is preprocessed. the spatial-temporal shape in NYC bike dataset is [1, 12, 16, 8], I think [16, 8] are the grid size of city given that the city is partitionized. but what does the value mean, the number of the bike? I can't find the related information.

And for the period data [3, 12, 16, 8], what does the value mean in this data. and does it mean every 3 day the data will be the same thing, why the period is 3?

Only by knowing the original information meaning, can I use my own dataset, and do the preprocessing accordingly. Thank you.

[tianlianghai]

I'd like to summarize my question:

1. what does the value in nyc bike mean? does it mean the number of bikes located at that block at that time?
2. what does the period data mean, since it has a period T=3, how does it related to the spatial temporal data?

[YuanYuan98]

1. The NYC bike dataset you are referring to is sourced from the NYC dataset. It represents bicycle traffic data. The values likely indicate the number of bicycles used in a particular block at a given time.

2. For example, the dataset, formatted as [3, 12, 16, 8], records data from the past 3 days for the corresponding time slot (one out of 12) for each location (covering $16 \times 8$ grid locations). Here, T is just a pre-defined parameter. UniST is designed to be flexible to different values of T by utilizing an attention mechanism, which adapts to various sequence lengths effectively.

This information should help you understand how to adjust your own dataset preprocessing.