Fast Adaptive Time Series ML Engine
This is an internal project - documentation is not updated anymore and substantially differ from the current API.
Explore the docs Β»
The Adaptive ML Engine that lets you build, deploy and update Models easily. An order of magnitude speed-up, combined with flexibility and rigour.
- 10x faster Adaptive Backtesting - What does that mean?
- Composite Models made Adaptive - What does that mean?
- Distributed computing - Why is this important?
- Update deployed models (coming in May) - Why is this important?
-
Prerequisites:
python >= 3.8
andpip
-
Install from pypi:
pip install fold-core
You can quickly train your chosen models and get predictions by running:
from sklearn.ensemble import RandomForestRegressor
from statsforecast.models import ARIMA
from fold import ExpandingWindowSplitter, train_evaluate
from fold.composites import Ensemble
from fold.transformations import OnlyPredictions
from fold.utils.dataset import get_preprocessed_dataset
X, y = get_preprocessed_dataset(
"weather/historical_hourly_la", target_col="temperature", shorten=1000
)
pipeline = [
Ensemble(
[
RandomForestRegressor(),
ARIMA(order=(1, 1, 0)),
]
),
OnlyPredictions(),
]
splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=0.2)
scorecard, prediction, trained_pipelines, _, _ = train_evaluate(pipeline, X, y, splitter)
(If you install krisi
by running pip install krisi
you get an extended report back, rather than a single metric.)
-
Adaptive Models and Backtesting at lightning speed.
β fold allows to simulate and evaluate your models like they would have performed, in reality/when deployed, with clever use of paralellization and design. -
Create composite models: ensembles, hybrids, stacking pipelines, easily.
β Underutilized, but the easiest, fastest way to increase performance of your Time Series models. -
Built with Distributed Computing in mind.
β Deploy your research and development pipelines to a cluster withray
, and usemodin
to handle out-of-memory datasets (full support for modin is coming in April). -
Bridging the gap between Online and Mini-Batch learning.
β Mix and matchxgboost
with ARIMA, in a single pipeline. Boost your model's accuracy by updating them on every timestamp, if desired. -
Update your deployed models, easily, as new data flows in.
β Real world is not static. Let your models adapt, without the need to re-train from scratch.
Name | Type | Dataset Type | Docs Link | Colab |
---|---|---|---|---|
β‘οΈ Core Walkthrough | Walkthrough | Energy | Notebook | Colab |
π Speed Comparison of Fold to other libraries | Walkthrough | Weather | Notebook | Colab |
π Example Collection | Example | Weather & Synthetic | Collection Link | - |
ποΈ Why we ended up building an Adaptive ML engine for Time Series | Blog | Public Release Blog Post | Blog post on Applied Exploration | - |
- Supports both Regression and Classification tasks.
- Online and Mini-batch learning.
- Feature selection and other transformations on an expanding/rolling window basis
- Use any scikit-learn/tabular model natively!
- Use any univariate or sequence models (wrappers provided in fold-wrappers).
- Use any Deep Learning Time Series models (wrappers provided in fold-wrappers).
- Super easy syntax!
- Probabilistic foreacasts (currently, for Classification, full support coming in April).
- Hyperparemeter optimization / Model selection. (coming in early April!)
It's like classical Backtesting / Time Series Cross-Validation, plus: Inside a test window, and during deployment, fold provides a way for models to update their parameters or access the last value. Learn more
Explore our Commercial License options here
Submit an issue or reach out to us on info at dream-faster.ai for any inquiries.
We want to bring much-needed transparency, speed and rigour to the process of creating Time Series ML pipelines, while also building a sustainable business, that can support the ecosystem in the long-term. Fold's licence is inbetween source-available and a traditional commercial software licence. It requires a paid licence for any commercial use, after the initial, 30 day trial period.
We also want to contribute to open research by giving free access to non-commercial, research use of fold
.
- No intermittent time series support, very limited support for missing values.
- No hierarchical time series support.