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).

## Legal Information

[*] Other names and brands may be claimed as the property of others.