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 or online
📚 Documentation
For complete details on features, API reference, CLI usage, and advanced configuration, visit our documentation site:
👉 https://manojmanivannan.github.io/ts-data-generator/
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
tsdataCLI for rapid prototyping or the Python API for production pipelines. - Schema Imputing: Reverse-engineer generation configs from existing CSV datasets.
Quickstart
Installation
pip install ts-data-generator
No install needed? Run the CLI directly with uv:
uvx --from ts-data-generator tsdata --helpUse
--from(not--with) because the package name (ts-data-generator) differs from the executable name (tsdata).
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()
License
MIT — see LICENSE.
1 maintainer
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