ts-data-generator 0.6.0

A Python library for generating synthetic time series data

Synthetic Time Series Data Generator

Generate realistic synthetic time series datasets with configurable dimensions, metrics, composable trend functions, and injectable anomalies — via a Python API or the tsdata CLI.

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📚 Documentation

For complete details on features, API reference, CLI usage, and advanced configuration, visit our documentation site:

👉 https://manojmanivannan.github.io/ts-data-generator/

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Features

• Realistic Data: Mimic real-world time series with trends, seasonality, and noise.
• Composable Trends: Layer multiple functions (Sinusoidal, Linear, AR Noise, Markov) to create complex signals.
• Injectable Anomalies: Simulate failures with point anomalies, missing data gaps, and concept drifts.
• Deterministic: Guaranteed reproducibility via a seedable RNG.
• CLI & API: Use the tsdata CLI for rapid prototyping or the Python API for production pipelines.
• Schema Imputing: Reverse-engineer generation configs from existing CSV datasets.

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Quickstart

Installation

pip install ts-data-generator

CLI Usage

tsdata generate --start 2024-01-01 --end 2024-01-07 --granularity h \
    --dims "region:US,EU,AP" \
    --mets "sales:LinearTrend(slope=45)+SinusoidalTrend(amplitude=10,freq=24)" \
    --output sales.csv

Python API

from ts_data_generator import DataGen
from ts_data_generator.utils.trends import SinusoidalTrend

dg = DataGen(seed=42)
dg.start_datetime = "2024-01-01"
dg.end_datetime = "2024-01-07"
dg.to_granularity("h")

dg.add_metric("temp", {SinusoidalTrend(amplitude=10, freq=24)})

df = dg.data
dg.plot()

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License

MIT — see LICENSE.