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Documentation Index

Fetch the complete documentation index at: https://docs.sulie.co/llms.txt

Use this file to discover all available pages before exploring further.

This section will guide you through the steps required to install, configure, and use Sulie to generate accurate forecasts for your time series data. Getting started with Sulie is straightforward and requires minimal setup.

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Create an Account

Start by creating an account through the Sulie dashboard. Once registered, navigate to the API Keys section where you can generate API keys for your organization. Each organization can have multiple API keys to manage different applications or environments. API keys are essential for authenticating your requests to the Sulie platform and accessing its forecasting and fine-tuning capabilities.

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Installation

Install the Sulie Python SDK using pip: 
pip3 install sulie
The SDK provides a clean, intuitive interface to interact with Sulie’s foundation models.

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Using the SDK

At the core of Sulie is the Dataset abstraction, which builds on Pandas DataFrames to represent your time series data. While Datasets can contain arbitrary data, they must include your target variable for forecasting. Here’s a simple example showing how to upload a dataset and generate forecasts: 
import os
import pandas as pd
from sulie import Sulie

client = Sulie(
    api_key=os.environ.get("SULIE_API_KEY")
)

# Prepare your data
df = pd.DataFrame(your_data)

# Upload a dataset
dataset = client.upload_dataset(
    name="product-purchases-v1", 
    df=df
)

# Forecast on time-series data                                                           
forecast = client.forecast(
    dataset="product-purchases-v1",
    horizon=30, # 30 time steps ahead
    target_col="y"
)