cesnet-tszoo 0.1.1

A toolkit for working with large time series network traffic datasets.

The goal of cesnet-tszoo project is to provide time series datasets with useful tools for preprocessing and reproducibility. Such as:

• API for downloading, configuring and loading CESNET-TimeSeries24, CESNET-AGG23 datasets. Each with various sources and aggregations.
• Example of configuration options:
  • Data can be split into train/val/test sets. Split can be done by time series or by time periods.
  • Transforming of data with built-in scalers or with custom scalers.
  • Handling missing values built-in fillers or with custom fillers.
• Creation and import of benchmarks, for easy reproducibility of experiments.
• Creation and import of annotations. Can create annotations for specific time series, specific time or specific time in specific time series.

Datasets

Name	CESNET-TimeSeries24	CESNET-AGG23
Published in	2025	2023
Collection duration	40 weeks	10 weeks
Collection period	9.10.2023 - 14.7.2024	25.2.2023 - 3.5.2023
Aggregation window	1 day, 1 hour, 10 min	1 min
Sources	CESNET3: Institutions, Institution subnets, IP addresses	CESNET2
Number of time series	Institutions: 849, Institution subnets: 1644, IP addresses: 825372	1
Cite	https://doi.org/10.1038/s41597-025-04603-x	https://doi.org/10.23919/CNSM59352.2023.10327823
Zenodo URL	https://zenodo.org/records/13382427	https://zenodo.org/records/8053021
Related papers

Installation

Install the package from pip with:

pip install cesnet-tszoo

or for editable install with:

pip install -e git+https://github.com/CESNET/cesnet-tszoo

Examples

Initialize dataset to create train, validation, and test dataframes

Using TimeBasedCesnetDataset dataset

from cesnet_tszoo.datasets import CESNET_TimeSeries24
from cesnet_tszoo.utils.enums import SourceType, AgreggationType
from cesnet_tszoo.configs import TimeBasedConfig

dataset = CESNET_TimeSeries24.get_dataset(data_root="/some_directory/", source_type=SourceType.INSTITUTIONS, aggregation=AgreggationType.AGG_1_DAY, is_series_based=False)
config = TimeBasedConfig(
    ts_ids=50, # number of randomly selected time series from dataset
    train_time_period=range(0, 100), 
    val_time_period=range(100, 150), 
    test_time_period=range(150, 250), 
    features_to_take=["n_flows", "n_packets"])
dataset.set_dataset_config_and_initialize(config)

train_dataframe = dataset.get_train_df()
val_dataframe = dataset.get_val_df()
test_dataframe = dataset.get_test_df()

Time-based datasets are configured with TimeBasedConfig.

Using SeriesBasedCesnetDataset dataset

from cesnet_tszoo.datasets import CESNET_TimeSeries24
from cesnet_tszoo.utils.enums import SourceType, AgreggationType
from cesnet_tszoo.configs import SeriesBasedConfig

dataset = CESNET_TimeSeries24.get_dataset(data_root="/some_directory/", source_type=SourceType.INSTITUTIONS, aggregation=AgreggationType.AGG_1_DAY, is_series_based=True)
config = SeriesBasedConfig(
    time_period=range(0, 250), 
    train_ts=100, # number of randomly selected time series from dataset
    val_ts=30, # number of randomly selected time series from dataset
    test_ts=20, # number of randomly selected time series from dataset
    features_to_take=["n_flows", "n_packets"])
dataset.set_dataset_config_and_initialize(config)

train_dataframe = dataset.get_train_df()
val_dataframe = dataset.get_val_df()
test_dataframe = dataset.get_test_df()

Series-based datasets are configured with SeriesBasedConfig.

Using load_benchmark

from cesnet_tszoo.benchmarks import load_benchmark

benchmark = load_benchmark(identifier="2e92831cb502", data_root="/some_directory/")
dataset = benchmark.get_initialized_dataset()

train_dataframe = dataset.get_train_df()
val_dataframe = dataset.get_val_df()
test_dataframe = dataset.get_test_df()

Whether loaded dataset is series-based or time-based depends on the benchmark. What can be loaded corresponds to previous datasets.

Papers