| Name | Usage |
|---|---|
| Naive | from fold_models import Naive |
| NaiveSeasonal | from fold_models import NaiveSeasonal |
| MovingAverage | from fold_models import MovingAverage |
| AR | from fold_models import AR |
| ARIMA | from fold_models import ARIMA |
-
Prerequisites:
python >= 3.7andpip -
Install from pypi:
pip install fold-models -
Depending on what model you'd like to wrap, you can either install the library directly or run
pip install "fold-models[<your_library_name>]"
You can quickly train your chosen models and get predictions by running:
from fold import ExpandingWindowSplitter, train_evaluate
from fold.utils.dataset import get_preprocessed_dataset
from fold_models import Naive
X, y = get_preprocessed_dataset(
"weather/historical_hourly_la", target_col="temperature", shorten=1000
)
model = Naive()
splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=50)
scorecard, predictions, trained_pipeline = train_evaluate(model, X, y, splitter)
If you want to try them out, we'd love to hear about your use case and help, please book a free 30-min call with us!
Join our Discord for live discussion!
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.