Datasets:
start
timestamp[us] | target
sequence | feat_static_cat
sequence | feat_dynamic_real
null | item_id
stringlengths 6
6
|
|---|---|---|---|---|
2012-01-01T01:00:00 | [-0.19363673541224083,-0.08851588245610625,-0.19363673541224083,-0.08851588245610581,-0.141076308934(...TRUNCATED) | [
0
] | null | MT_001 |
2012-01-01T01:00:00 | [-0.7670478012651036,-0.711666063830166,-0.7393569325476368,-0.8778112761349713,-0.9055021448524383,(...TRUNCATED) | [
1
] | null | MT_002 |
2012-01-01T01:00:00 | [5.979014764830118,5.979014764830118,5.979014764830118,5.979014764830118,5.979014764830118,3.4501017(...TRUNCATED) | [
2
] | null | MT_003 |
2012-01-01T01:00:00 | [0.7505563515381487,0.7239395037700361,0.7106310798859866,-0.19434174422996173,-0.7266786995922804,-(...TRUNCATED) | [
3
] | null | MT_004 |
2012-01-01T01:00:00 | [1.2971192259991395,1.1747047180437682,0.964851275834564,0.03799857274390112,-0.25929380371914146,-0(...TRUNCATED) | [
4
] | null | MT_005 |
2012-01-01T01:00:00 | [2.5625262188234315,2.419487032222773,1.5731718448355245,0.05934045331186476,-0.5247362253075035,-0.(...TRUNCATED) | [
5
] | null | MT_006 |
2012-01-01T01:00:00 | [0.3800059977182203,0.13153345303024824,0.0901213622489197,-0.1790572278297153,-0.36541163634569435,(...TRUNCATED) | [
6
] | null | MT_007 |
2012-01-01T01:00:00 | [0.42068878220555556,0.36244023053898267,-0.2637316998768013,-0.9481521819591582,-1.2976434919586723(...TRUNCATED) | [
7
] | null | MT_008 |
2012-01-01T01:00:00 | [0.9366519850737007,0.6665218837317595,0.5002879752136429,-0.5594531915893506,-0.3516608059417032,-0(...TRUNCATED) | [
8
] | null | MT_009 |
2012-01-01T01:00:00 | [1.2370097894560281,1.0163219779980743,0.6169821286932055,-0.39187643797172883,-0.5390016456103648,-(...TRUNCATED) | [
9
] | null | MT_010 |
End of preview. Expand
in Dataset Viewer.
Download the Dataset:
from datasets import load_dataset
dataset = load_dataset("LeoTungAnh/electricity_hourly")
Dataset Card for Electricity Consumption
This dataset encompasses hourly electricity consumption in kilowatts (kW) across a span of three years (2012-2014), involving 370 individual clients in Portugal.
Preprocessing information:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
Dataset information:
- Number of time series: 370
- Number of training samples: 26208
- Number of validation samples: 26256 (number_of_training_samples + 48)
- Number of testing samples: 26304 (number_of_validation_samples + 48)
Dataset format:
Dataset({
features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
num_rows: 370
})
Data format for a sample:
'start': datetime.datetime
'target': list of a time series data
'feat_static_cat': time series index
'feat_dynamic_real': None
'item_id': name of time series
Data example:
{'start': datetime.datetime(2012, 1, 1, 1, 0),
'target': [-0.19363673541224083, -0.08851588245610625, -0.19363673541224083, ... -0.5615597207587115,...],
'feat_static_cat': [0],
'feat_dynamic_real': None,
'item_id': 'MT_001'
}
Usage:
- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
- Other algorithms can extract data directly by making use of 'target' feature.
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