Comments (2)
"instance" means "sample". The difference between instances and features is that instances generally come from the same distribution. For example, for stock market, n_instance is the number of stocks, and n_features is the number of features (e.g. open, high, low, close, volume). For traffic flow, n_instance can be the number of roads, and n_features is the number of features collected on the road.
For a multivariate time series whose data shape is "n_variables x n_timestamps", the input shape may be either "1 x n_timestamps x n_variables" or "n_variables x n_timestamps x 1". It depends on whether these variables are features or samples.
from ts2vec.
Thank you for the clarification. I had not read the recent papers using contrastive loss, I am catching up. This is good work, thank you.
from ts2vec.
Related Issues (20)
- 下游任务相关问题?
- The dataset about ETT HOT 2
- Question on Instance Constrastive-Loss
- Q: Multivariate time-series regression HOT 1
- prediction part
- ETTh1 data dimension mismatch problem HOT 2
- 在得到数据的表征后,比如分类数据,相同类数据得到的表征相似度会比非同类高吗? HOT 1
- Suitability of ts2vec Framework for Lengthy Mono Audio Waveforms HOT 1
- Incorrect results when using MPS backend (MacOS)
- Pretrained model loading problem HOT 1
- Evaluate code for univariate forcasting task maybe is not right. There is information leaking. HOT 4
- Evaluate code for univariate forecasting task maybe there is information leaking. HOT 7
- 关于损失函数的问题
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from ts2vec.