| Name | functime JSON |
| Version |
0.9.4
JSON |
| download |
| home_page | None |
| Summary | Time-series machine learning at scale. |
| upload_time | 2023-12-28 08:23:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
<div align="center">
<h1>Time-series machine learning at scale</h1>
<br />
[](https://pypi.org/project/functime/)
[](https://pypi.org/project/functime/)
[](https://github.com/psf/black)
[](https://github.com/TracecatHQ/functime/actions/workflows/publish.yml)
[](https://github.com/TracecatHQ/functime/actions/workflows/quickstart.yml)
[](https://discord.gg/JKMrZKjEwN)
</div>
---
**functime** is a powerful [Python library](https://pypi.org/project/functime/) for production-ready **global forecasting** and **time-series feature extraction** on **large panel datasets**.
**functime** also comes with time-series [preprocessing](https://docs.functime.ai/ref/preprocessing/) (box-cox, differencing etc), cross-validation [splitters](https://docs.functime.ai/ref/cross-validation/) (expanding and sliding window), and forecast [metrics](https://docs.functime.ai/ref/metrics/) (MASE, SMAPE etc). All optimized as [lazy Polars](https://pola-rs.github.io/polars-book/user-guide/lazy/using/) transforms.
Join us on [Discord](https://discord.gg/JKMrZKjEwN)!
## Highlights
- **Fast:** Forecast and extract features (e.g. tsfresh, Catch22) across 100,000 time series in seconds *on your laptop*
- **Efficient:** Embarrassingly parallel feature engineering for time-series using [`Polars`](https://www.pola.rs/)
- **Battle-tested:** Machine learning algorithms that deliver real business impact and win competitions
- **Exogenous features:** supported by every forecaster
- **Backtesting** with expanding window and sliding window splitters
- **Automated lags and hyperparameter tuning** using [`FLAML`](https://github.com/microsoft/FLAML)
## Additional Highlights
`functime` comes with a specialized LLM agent to analyze, describe, and compare your forecasts. Check out the walkthrough [here](https://docs.functime.ai/notebooks/llm/).
## Getting Started
Install `functime` via the [pip](https://pypi.org/project/functime) package manager.
```bash
pip install functime
```
`functime` comes with extra options. For example, to install `functime` with large-language model (LLM) and lightgbm features:
```bash
pip install "functime[llm,lgb]"
```
- `cat`: To use `catboost` forecaster
- `xgb`: To use `xgboost` forecaster
- `lgb`: To use `lightgbm` forecaster
- `llm`: To use the LLM-powered forecast analyst
### Forecasting
```python
import polars as pl
from functime.cross_validation import train_test_split
from functime.seasonality import add_fourier_terms
from functime.forecasting import linear_model
from functime.preprocessing import scale
from functime.metrics import mase
# Load commodities price data
y = pl.read_parquet("https://github.com/TracecatHQ/functime/raw/main/data/commodities.parquet")
entity_col, time_col = y.columns[:2]
# Time series split
y_train, y_test = y.pipe(train_test_split(test_size=3))
# Fit-predict
forecaster = linear_model(freq="1mo", lags=24)
forecaster.fit(y=y_train)
y_pred = forecaster.predict(fh=3)
# functime ❤️ functional design
# fit-predict in a single line
y_pred = linear_model(freq="1mo", lags=24)(y=y_train, fh=3)
# Score forecasts in parallel
scores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)
# Forecast with target transforms and feature transforms
forecaster = linear_model(
freq="1mo",
lags=24,
target_transform=scale(),
feature_transform=add_fourier_terms(sp=12, K=6)
)
# Forecast with exogenous regressors!
# Just pass them into X
X = (
y.select([entity_col, time_col])
.pipe(add_fourier_terms(sp=12, K=6)).collect()
)
X_train, X_future = y.pipe(train_test_split(test_size=3))
forecaster = linear_model(freq="1mo", lags=24)
forecaster.fit(y=y_train, X=X_train)
y_pred = forecaster.predict(fh=3, X=X_future)
```
View the full walkthrough on forecasting [here](https://docs.functime.ai/forecasting/).
### Feature Extraction
`functime` comes with over 100+ [time-series feature extractors](https://docs.functime.ai/feature-extraction/).
Every feature is easily accessible via `functime`'s custom `ts` (time-series) namespace, which works with any `Polars` Series or expression. To register the custom `ts` `Polars` namespace, you must first import `functime` in your module.
To register the custom `ts` `Polars` namespace, you must first import `functime`!
```python
import polars as pl
import numpy as np
from functime.feature_extractors import FeatureExtractor, binned_entropy
# Load commodities price data
y = pl.read_parquet("https://github.com/TracecatHQ/functime/raw/main/data/commodities.parquet")
# Get column names ("commodity_type", "time", "price")
entity_col, time_col, value_col = y.columns
# Extract a single feature from a single time-series
binned_entropy = binned_entropy(
pl.Series(np.random.normal(0, 1, size=10)),
bin_count=10
)
# 🔥 Also works on LazyFrames with query optimization
features = (
pl.LazyFrame({
"index": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"value": np.random.normal(0, 1, size=10)
})
.select(
binned_entropy=pl.col("value").ts.binned_entropy(bin_count=10),
lempel_ziv_complexity=pl.col("value").ts.lempel_ziv_complexity(threshold=3),
longest_streak_above_mean=pl.col("value").ts.longest_streak_above_mean(),
)
.collect()
)
# 🚄 Extract features blazingly fast on many
# stacked time-series using `group_by`
features = (
y.group_by(entity_col)
.agg(
binned_entropy=pl.col(value_col).ts.binned_entropy(bin_count=10),
lempel_ziv_complexity=pl.col(value_col).ts.lempel_ziv_complexity(threshold=3),
longest_streak_above_mean=pl.col(value_col).ts.longest_streak_above_mean(),
)
)
# 🚄 Extract features blazingly fast on windows
# of many time-series using `group_by_dynamic`
features = (
# Compute rolling features at yearly intervals
y.group_by_dynamic(
time_col,
every="12mo",
by=entity_col,
)
.agg(
binned_entropy=pl.col(value_col).ts.binned_entropy(bin_count=10),
lempel_ziv_complexity=pl.col(value_col).ts.lempel_ziv_complexity(threshold=3),
longest_streak_above_mean=pl.col(value_col).ts.longest_streak_above_mean(),
)
)
```
## Related Projects
If you are interested in general data-science related plugins for `Polars`, you must check out [`polars-ds`](https://github.com/abstractqqq/polars_ds_extension). `polars-ds` is a project created by one of `functime`'s core maintainers and is the easiest way to extend your `Polars` pipelines with commonly used data-science operations made blazing fast with Rust!
## License
`functime` is distributed under [Apache-2.0](LICENSE).
Raw data
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"description": "<div align=\"center\">\n <h1>Time-series machine learning at scale</h1>\n<br />\n\n\n[](https://pypi.org/project/functime/)\n[](https://pypi.org/project/functime/)\n[](https://github.com/psf/black)\n[](https://github.com/TracecatHQ/functime/actions/workflows/publish.yml)\n[](https://github.com/TracecatHQ/functime/actions/workflows/quickstart.yml)\n[](https://discord.gg/JKMrZKjEwN)\n\n</div>\n\n---\n**functime** is a powerful [Python library](https://pypi.org/project/functime/) for production-ready **global forecasting** and **time-series feature extraction** on **large panel datasets**.\n\n**functime** also comes with time-series [preprocessing](https://docs.functime.ai/ref/preprocessing/) (box-cox, differencing etc), cross-validation [splitters](https://docs.functime.ai/ref/cross-validation/) (expanding and sliding window), and forecast [metrics](https://docs.functime.ai/ref/metrics/) (MASE, SMAPE etc). All optimized as [lazy Polars](https://pola-rs.github.io/polars-book/user-guide/lazy/using/) transforms.\n\nJoin us on [Discord](https://discord.gg/JKMrZKjEwN)!\n\n## Highlights\n- **Fast:** Forecast and extract features (e.g. tsfresh, Catch22) across 100,000 time series in seconds *on your laptop*\n- **Efficient:** Embarrassingly parallel feature engineering for time-series using [`Polars`](https://www.pola.rs/)\n- **Battle-tested:** Machine learning algorithms that deliver real business impact and win competitions\n- **Exogenous features:** supported by every forecaster\n- **Backtesting** with expanding window and sliding window splitters\n- **Automated lags and hyperparameter tuning** using [`FLAML`](https://github.com/microsoft/FLAML)\n\n## Additional Highlights\n`functime` comes with a specialized LLM agent to analyze, describe, and compare your forecasts. Check out the walkthrough [here](https://docs.functime.ai/notebooks/llm/).\n\n## Getting Started\nInstall `functime` via the [pip](https://pypi.org/project/functime) package manager.\n```bash\npip install functime\n```\n\n`functime` comes with extra options. For example, to install `functime` with large-language model (LLM) and lightgbm features:\n\n```bash\npip install \"functime[llm,lgb]\"\n```\n\n- `cat`: To use `catboost` forecaster\n- `xgb`: To use `xgboost` forecaster\n- `lgb`: To use `lightgbm` forecaster\n- `llm`: To use the LLM-powered forecast analyst\n\n### Forecasting\n\n```python\nimport polars as pl\nfrom functime.cross_validation import train_test_split\nfrom functime.seasonality import add_fourier_terms\nfrom functime.forecasting import linear_model\nfrom functime.preprocessing import scale\nfrom functime.metrics import mase\n\n# Load commodities price data\ny = pl.read_parquet(\"https://github.com/TracecatHQ/functime/raw/main/data/commodities.parquet\")\nentity_col, time_col = y.columns[:2]\n\n# Time series split\ny_train, y_test = y.pipe(train_test_split(test_size=3))\n\n# Fit-predict\nforecaster = linear_model(freq=\"1mo\", lags=24)\nforecaster.fit(y=y_train)\ny_pred = forecaster.predict(fh=3)\n\n# functime \u2764\ufe0f functional design\n# fit-predict in a single line\ny_pred = linear_model(freq=\"1mo\", lags=24)(y=y_train, fh=3)\n\n# Score forecasts in parallel\nscores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)\n\n# Forecast with target transforms and feature transforms\nforecaster = linear_model(\n freq=\"1mo\",\n lags=24,\n target_transform=scale(),\n feature_transform=add_fourier_terms(sp=12, K=6)\n)\n\n# Forecast with exogenous regressors!\n# Just pass them into X\nX = (\n y.select([entity_col, time_col])\n .pipe(add_fourier_terms(sp=12, K=6)).collect()\n)\nX_train, X_future = y.pipe(train_test_split(test_size=3))\nforecaster = linear_model(freq=\"1mo\", lags=24)\nforecaster.fit(y=y_train, X=X_train)\ny_pred = forecaster.predict(fh=3, X=X_future)\n```\n\nView the full walkthrough on forecasting [here](https://docs.functime.ai/forecasting/).\n\n### Feature Extraction\n\n`functime` comes with over 100+ [time-series feature extractors](https://docs.functime.ai/feature-extraction/).\nEvery feature is easily accessible via `functime`'s custom `ts` (time-series) namespace, which works with any `Polars` Series or expression. To register the custom `ts` `Polars` namespace, you must first import `functime` in your module.\n\nTo register the custom `ts` `Polars` namespace, you must first import `functime`!\n\n```python\nimport polars as pl\nimport numpy as np\nfrom functime.feature_extractors import FeatureExtractor, binned_entropy\n\n# Load commodities price data\ny = pl.read_parquet(\"https://github.com/TracecatHQ/functime/raw/main/data/commodities.parquet\")\n\n# Get column names (\"commodity_type\", \"time\", \"price\")\nentity_col, time_col, value_col = y.columns\n\n# Extract a single feature from a single time-series\nbinned_entropy = binned_entropy(\n pl.Series(np.random.normal(0, 1, size=10)),\n bin_count=10\n)\n\n# \ud83d\udd25 Also works on LazyFrames with query optimization\nfeatures = (\n pl.LazyFrame({\n \"index\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n \"value\": np.random.normal(0, 1, size=10)\n })\n .select(\n binned_entropy=pl.col(\"value\").ts.binned_entropy(bin_count=10),\n lempel_ziv_complexity=pl.col(\"value\").ts.lempel_ziv_complexity(threshold=3),\n longest_streak_above_mean=pl.col(\"value\").ts.longest_streak_above_mean(),\n )\n .collect()\n)\n\n# \ud83d\ude84 Extract features blazingly fast on many\n# stacked time-series using `group_by`\nfeatures = (\n y.group_by(entity_col)\n .agg(\n binned_entropy=pl.col(value_col).ts.binned_entropy(bin_count=10),\n lempel_ziv_complexity=pl.col(value_col).ts.lempel_ziv_complexity(threshold=3),\n longest_streak_above_mean=pl.col(value_col).ts.longest_streak_above_mean(),\n )\n)\n\n# \ud83d\ude84 Extract features blazingly fast on windows\n# of many time-series using `group_by_dynamic`\nfeatures = (\n # Compute rolling features at yearly intervals\n y.group_by_dynamic(\n time_col,\n every=\"12mo\",\n by=entity_col,\n )\n .agg(\n binned_entropy=pl.col(value_col).ts.binned_entropy(bin_count=10),\n lempel_ziv_complexity=pl.col(value_col).ts.lempel_ziv_complexity(threshold=3),\n longest_streak_above_mean=pl.col(value_col).ts.longest_streak_above_mean(),\n )\n)\n\n```\n\n## Related Projects\n\nIf you are interested in general data-science related plugins for `Polars`, you must check out [`polars-ds`](https://github.com/abstractqqq/polars_ds_extension). `polars-ds` is a project created by one of `functime`'s core maintainers and is the easiest way to extend your `Polars` pipelines with commonly used data-science operations made blazing fast with Rust!\n\n## License\n`functime` is distributed under [Apache-2.0](LICENSE).\n\n",
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