time-series-forecasting-electricity-0001¶
Use Case and High-Level Description¶
This is a Time Series Forecasting model based on the Temporal Fusion Transformer and model trained on the Electricity dataset.
Specification¶
Metric |
Value |
|---|---|
GOps |
0.40 |
MParams |
2.26 |
Source framework |
PyTorch* |
Accuracy¶
Metric |
Value |
|---|---|
Normalized Quantile Loss (P50) |
0.056 |
Normalized Quantile Loss (P90) |
0.028 |
Normalized Quantile Loss described in Bryan Lim et al..
The quality metrics were calculated on the Electricity dataset (test split).
Input¶
name: timestamps
shape: 1, 192, 5
format: B, T, N
B - batch size.
T - number of input timestamps.
N - number of input features.
Output¶
name: quantiles
shape: 1, 24, 3
format: B, T, Q
B - batch size.
T - number of output timestamps.
Q - number of output quantiles (0.1, 0.5, 0.9).
Demo usage¶
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
Legal Information¶
[*] Other names and brands may be claimed as the property of others.