Time Series Forecasting Tool for Machine Learning Researchers with Nerual Process
Project description
Sibyl: A Time-Seires Forecasting Package
Sibyl is a package for forecasting time series data with PyTorchLightning which is a lightweight PyTorch wrapper for Machine Learning Researchers.
Sibyl is released and maintained by Microsoft Substrate Connectivity Team.
Models
- Seq to Seq
- Long-short term memory
- Neural process
- Attentive neural process
Installation
Sibyl is avaialbe on PyPi, feel free to get it via pip
:
pip install mssibyl
Quick Start
UPDATING ...
import pandas as pd
from mssibyl.models import BaseModel
from mssibyl.trainer import BaseTrainer
from mssibyl.datasets.utils import make_timeline
df = pd.read_csv("./data.csv")
df.columns = ["ds", "y"]
df_with_future = make_timeline(df, freq="H", periods=300)
args = {
"lookback": 48,
"forecast_horizon": 1,
"data": df_with_future,
"target": "y",
}
sibyl_model = BaseModel(args)
trainer = BaseTrainer(
max_epochs=100, gpus=1, show_progress_bar=False, early_stop_callback=True
)
trainer.fit(sibyl_model)
preds = sibyl_model.predict(interval=True, include_history=True)
preds.to_csv("res.csv", index=False)
Changelog
Version 0.1 (coming)
- Initial release
License
Sibyl is licensed under the MIT license
Project details
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