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.
- Multivariate Dimension Expansion: Expand dimensions to their full Cartesian product (
expand_dimensions) with independent, reproducible per-combination metric series and per-dimension control. - Correlated Multi-Items: Generate linked dimension tuples and correlated metric groups simultaneously (
add_multi_items). - 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
# Multivariate expansion with explicit scaling and stochastic variance
tsdata generate --start 2024-01-01 --end 2024-01-07 --granularity h \
--dims "region=random_choice(US,EU),weights={US:5,EU:2}" \
--dims "env=random_choice(prod,dev)" \
--mets "sales:LinearTrend(slope=45)+SinusoidalTrend(amplitude=10,freq=24)" \
--expand-dimensions \
--scale-variance 0.2 \
--output expanded_sales.csv
Python API
from ts_data_generator import DataGen
from ts_data_generator.utils.functions import random_choice
from ts_data_generator.utils.trends import SinusoidalTrend, LinearTrend
# Expand dimensions across Cartesian product with scaled multi-series metrics
dg = DataGen(
start_datetime="2024-01-01",
end_datetime="2024-01-07",
granularity="h",
seed=42,
expand_dimensions=True,
scale_variance=0.2,
)
# Explicit dimension weights scale metrics differently per category slice
dg.add_dimension("tier", {"enterprise": 10.0, "pro": 3.0, "free": 1.0})
dg.add_dimension("region", random_choice(["US", "EU"]), weights={"US": 5.0, "EU": 2.0})
dg.add_dimension("environment", random_choice(["prod", "dev"]))
dg.add_metric("sales", {LinearTrend(offset=100, slope=10), SinusoidalTrend(amplitude=20, freq=24)})
# Linked correlated columns with tuple weights
dg.add_multi_items(
names=["city", "country"],
function=[("New York", "US"), ("London", "UK")],
weights={("New York", "US"): 4.0, ("London", "UK"): 2.0},
)
df = dg.data
print(f"Generated {len(df)} rows across {df.groupby(['tier', 'region', 'environment', 'city']).ngroups} combinations")
dg.plot()
License
MIT — see LICENSE.
1 maintainer
Release files for ts-data-generator 0.6.6
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