Integrate NeuralProphet's powerful time series forecasting capabilities into your ZenML pipelines for seamless and efficient modeling of temporal data. This integration enables you to leverage NeuralProphet's state-of-the-art algorithms and intuitive API within the structured framework of ZenML, streamlining your time series modeling workflow.
from zenml import pipeline, step
import pandas as pd
from neuralprophet import NeuralProphet
@step
def load_data() -> pd.DataFrame:
# Load your time series data here
data = pd.read_csv('time_series_data.csv')
return data
@step
def trainer_step(data: pd.DataFrame) -> NeuralProphet:
model = NeuralProphet()
model.fit(data, freq="D")
return model
@pipeline
def time_series_pipeline():
data = load_data()
trainer_step(data)
if __name__ == "__main__":
time_series_pipeline()
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