Skip to main content

A Python library for generating synthetic time series data

Project description

Synthetic Time Series Data Generator

Python CI

A Python library for generating synthetic time series data

Special thanks to: Nike-Inc repo

MarineGEO circle logo

Installation

PyPi (recommended)

You can install with pip directly by

pip install ts-data-generator

Repo

After cloning this repo and creating a virtual environment, run the following command:

pip install --editable .

Usage

  1. To check out constructing for time series data, check the sample notebook here
  2. To extract the trends from an existing data, check this sample notebook here

UV

You can easily run it using uv

uvx --python 3.11 --from ts-data-generator tsdata generate \
    --start "2019-01-01" \
    --end "2019-01-12" \
    --granularity "5min" \
    --dims "product_id:random_float:1,4" \
    --dims "const:constant:5" 
    --mets "sales:LinearTrend(limit=500)+WeekendTrend(weekend_effect=50)" 
    --mets "trend:LinearTrend(limit=10)" 
    --output "data.csv"

CLI

You can also use the command line utility tsdata to generate the data.

~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ tsdata                  
Usage: tsdata [OPTIONS] COMMAND [ARGS]...

  CLI tool for generating time series data.

Options:
  --help  Show this message and exit.

Commands:
  dimensions  List all available dimension functions in ts_data_generator.utils.functions.
  generate    Generate time series data and save it to a CSV file.
  metrics     List all available metric trends in ts_data_generator.utils.trends.
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ 
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ tsdata dimensions       
Available dimension functions are:
- auto_generate_name(category: str) -> str
        Example: name:auto_generate_name:mycat
- constant(value: Union[int, str, float, list])
        Example: name:constant:10
- ordered_choice(iterable)
        Example: name:ordered_choice:A,B,C
- random_choice(iterable: Iterable[~T]) -> Generator[~T, NoneType, NoneType]
        Example: name:random_choice:A,B,C
- random_float(start: float, end: float)
        Example: name:random_float:0.0,1.0
- random_int(start: int, end: int) -> Generator[int, NoneType, NoneType]
        Example: name:random_int:1,100
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ 
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ tsdata metrics   
Available metric trends & parameters are:
- LinearTrend(name: str = 'default', offset: float = 0.0, noise_level: float = 0.0, limit: float = 2.0)
        Example: sales:LinearTrend(offset=0,noise_level=1,limit=10)
- SinusoidalTrend(name: str = 'default', amplitude: float = 1, freq: float = 1, phase: float = 0, noise_level: float = 0)
        Example: sales:SinusoidalTrend(amplitude=1,freq=24,phase=0,noise_level=0)
- StockTrend(name: str = 'default', amplitude: float = 15.0, direction: Literal['up', 'down'] = 'up', noise_level: float = 0.0)
        Example: sales:StockTrend(amplitude=15.0,direction='up',noise_level=0.0)
- WeekendTrend(name: str = 'default', weekend_effect: float = 1.0, direction: Literal['up', 'down'] = 'up', noise_level: float = 0.0, limit: float = 10.0)
        Example: sales:WeekendTrend(weekend_effect=10,direction='up',noise_level=0.5,limit=10)
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ 
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ tsdata generate
Usage: tsdata generate [OPTIONS]

  Generate time series data and save it to a CSV file.

Options:
  --start TEXT                    Start datetime 'YYYY-MM-DD'
  --end TEXT                      End datetime 'YYYY-MM-DD'
  --granularity [s|min|5min|h|D|W|ME|Y]
                                  Granularity of the time series data
  --dims TEXT                     + separated list of dimensions definition of format 'name:function:values'
  --mets TEXT                     + separated list of metrics definition trends of format 'name:trend(*params)'
  --output TEXT                   Output file name
  --help                          Show this message and exit.
~/G/ts-data-generator on  main! 🐍 (ts-data-generator) $ 

For example you can call this cli tool like below to generate data

tsdata generate \
  --start "2019-01-01" \
  --end "2019-01-12" \
  --granularity "5min" \
  --dims "product:random_choice:A,B,C,D" \
  --dims "product_id:random_float:1,4" \
  --dims "const:constant:5" \
  --mets "sales:LinearTrend(limit=500)+WeekendTrend(weekend_effect=50)" \
  --mets "trend:LinearTrend(limit=10)" \
  --output "data.csv"

Release method

  1. git tag <x.x.x>
  2. git push origin <x.x.x>

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ts_data_generator-0.2.5.tar.gz (804.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ts_data_generator-0.2.5-py3-none-any.whl (20.1 kB view details)

Uploaded Python 3

File details

Details for the file ts_data_generator-0.2.5.tar.gz.

File metadata

  • Download URL: ts_data_generator-0.2.5.tar.gz
  • Upload date:
  • Size: 804.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ts_data_generator-0.2.5.tar.gz
Algorithm Hash digest
SHA256 9546271bb2dba79ec7b3ce353b0885b8d55deb56233e5a9d6f670e1b5febc457
MD5 de1764a068bb28e15e6c746ed5e7e19b
BLAKE2b-256 118eac54c3e8996336c9489cda950970becd033ed02cd61a93690672cf4541b8

See more details on using hashes here.

Provenance

The following attestation bundles were made for ts_data_generator-0.2.5.tar.gz:

Publisher: ci.yaml on manojmanivannan/ts-data-generator

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ts_data_generator-0.2.5-py3-none-any.whl.

File metadata

File hashes

Hashes for ts_data_generator-0.2.5-py3-none-any.whl
Algorithm Hash digest
SHA256 26acea58778f333dffd5fa683336790e3b62d80ff7a66d196110c0d3c99a9ba2
MD5 50eeb3a38550843e5e28ef4d0b9d8461
BLAKE2b-256 7bde9fc97a7d48d55cdb6fb87e2797e52467c96c6dab3a5bddf2992eadc06f58

See more details on using hashes here.

Provenance

The following attestation bundles were made for ts_data_generator-0.2.5-py3-none-any.whl:

Publisher: ci.yaml on manojmanivannan/ts-data-generator

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page