timeseriesflow 0.2.1

Entity-based time-series processing framework for Python

TimeSeriesFlow

Entity-based time-series processing for Python, plus AdaptiveForecast for profile-driven model architecture selection.

Write one function per entity (sensor_id, device_id, customer_id, ...). The framework handles grouping, retries, checkpointing, progress, memory tracking, and logging.

Package	Role
timeseriesflow	Per-entity processing, EntityRunner, CLI (tsflow), checkpoints
adaptiveforecast	Series profiling and forecasting architecture recommendations

What is AdaptiveForecast?

AdaptiveForecast answers one question before you train anything:

Given this time series, which forecasting approach is worth trying first?

It profiles volatility, trend, seasonality, gaps, spikes, and data quality, then returns ranked recommendations (moving_average, exponential_smoothing, lstm, ...). Rules are deterministic, not LLM-based.

Situation	How it helps
Many sensors or devices	Pick a model family per entity, not one global default
Mixed data quality	ValidationGate flags series too short or noisy to forecast
Team handoffs	reason fields explain each recommendation
With TimeSeriesFlow	Profile inside @entity_flow (combined workflow)

It does not train models or produce forecasts. It recommends architecture recipes only.

Install

v0.2.0 from PyPI:

pip install timeseriesflow

Pin a release or install from git:

pip install timeseriesflow==0.2.0
pip install git+https://github.com/baban9/timeseriesflow.git@v0.2.0

Development install:

git clone https://github.com/baban9/timeseriesflow.git
cd timeseriesflow
pip install -e ".[dev]"

Requires Python 3.10+.

Quick start

Recommended API: @entity_flow for logic, EntityRunner or tsflow run for production.

import pandas as pd
from timeseriesflow import EntityContext, entity_flow

df = pd.DataFrame(
    {
        "sensor_id": ["S1", "S1", "S1", "S2", "S2", "S2"],
        "timestamp": pd.date_range("2024-01-01", periods=6, freq="h", tz="UTC"),
        "value": [1.0, 2.0, 3.0, 10.0, 11.0, 12.0],
    }
)

@entity_flow(entity_key="sensor_id", time_key="timestamp")
def process_sensor(df: pd.DataFrame, ctx: EntityContext) -> dict[str, object]:
    return {
        "sensor_id": ctx.entity_id,
        "rows": len(df),
        "mean_value": float(df["value"].mean()),
    }

result = process_sensor.run(df)
print(f"succeeded={result.succeeded} failed={result.failed}")
print(result.outputs)

Expected output:

succeeded=2 failed=0
[{'sensor_id': 'S1', 'rows': 3, 'mean_value': 2.0}, {'sensor_id': 'S2', 'rows': 3, 'mean_value': 11.0}]

CLI

tsflow run examples/basic_pipeline.py
tsflow validate examples/basic_pipeline.py
tsflow info
tsflow checkpoint-status --checkpoint-dir ./checkpoints

AdaptiveForecast

from adaptiveforecast import ProfileAwareArchitectureSelection

result = ProfileAwareArchitectureSelection(
    time_column="timestamp",
    value_column="value",
    max_models=3,
).select(df)

print(result.best_model)
print(result.recommendation.to_dict())

Examples

python examples/advise_and_process.py          # TimeSeriesFlow + AdaptiveForecast
python examples/adaptiveforecast_profile.py  # profiling only
python examples/architecture_selection.py    # full selection workflow

Legacy API: Flow + FlowConfig still works. New projects should use @entity_flow. Deprecation warning planned for v0.2.

Features

TimeSeriesFlow: entity grouping, retries, JSONL checkpoints, Rich progress, memory tracking, CSVSource / ParquetSource, tsflow CLI.

AdaptiveForecast: series profiling, validation gate, seven recipes (naive through cnn_lstm), explainable ModelAdvisor rules.

Documentation

Topic	Link
Golden path (recommended)	docs/golden_path.md
Combined TS + AdaptiveForecast	docs/combined_workflow.md
AdaptiveForecast intro	docs/adaptiveforecast.md
Sparse time series	docs/sparse_series.md
Architecture selection	docs/architecture_selection.md
Getting started	docs/getting_started.md
EntityRunner	docs/runner.md
CLI	docs/cli.md
Public API	docs/api.md
Changelog	CHANGELOG.md
Publishing releases	docs/publishing.md

Development

pytest
ruff check src tests examples
mypy src

Contributing

main is protected. Open a pull request with review; do not push directly to main.

git checkout -b feat/my-change
git push -u origin feat/my-change
gh pr create --fill

See CONTRIBUTING.md and branch protection guide.

License

MIT. See LICENSE.