metadata
dataset_info:
features:
- name: start
dtype: timestamp[s]
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
- name: target
sequence: float64
splits:
- name: train
num_bytes: 120352440
num_examples: 862
- name: validation
num_bytes: 120683448
num_examples: 862
- name: test
num_bytes: 121014456
num_examples: 862
download_size: 124542918
dataset_size: 362050344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Dataset Card for "traffic_hourly"
Download the Dataset:
from datasets import load_dataset
dataset = load_dataset("LeoTungAnh/traffic_hourly")
Dataset Card for Electricity Consumption
this dataset encompasses 862 hourly time series data points revealing the road occupancy rates across freeways in the San Francisco Bay area from 2015 to 2016.
Preprocessing information:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
Dataset information:
- Number of time series: 862
- Number of training samples: 17448
- Number of validation samples: 17496 (number_of_training_samples + 48)
- Number of testing samples: 17544 (number_of_validation_samples + 48)
Dataset format:
Dataset({
features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
num_rows: 862
})
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(2015, 1, 1, 0, 0, 1),
'feat_static_cat': [0],
'feat_dynamic_real': None,
'item_id': 'T1',
'target': [-0.7127609544951682, -0.6743409178438863, -0.3749847989359815, ... 0.12447567753068307,...]
}
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.