Forecasting

Sulie’s SDK simplifies time series forecasting, offering both zero-shot and fine-tuned models through the Mimosa foundation model. The All-in-One plan includes 100,000 inference calls, covering both types of forecasting models.

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Basic Usage

The example below demonstrates how to forecast renewable energy demand using Sulie’s forecast method.

import os
import pandas as pd
from sulie import Sulie

# Initialize Sulie client with an API key
client = Sulie(api_key=os.environ.get("SULIE_API_KEY"))

# Sample data for renewable energy demand
df = pd.DataFrame({
    "timestamp": pd.date_range(start="2023-01-01", periods=1000, freq="H"),
    "solar_demand": [...],  # Hourly solar energy demand
    "wind_demand": [...],   # Hourly wind energy demand
    "location": ["Plant A", "Plant B", ...]  # Location identifiers for different plants
})

# Forecasting solar demand per plant
forecast = client.forecast(
    dataset=df,
    target_col="solar_demand",
    id_col="location",
    timestamp_col="timestamp",
    frequency="H",
    horizon=24,        # Forecast the next 24 hours
)

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Parameters

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Output

The output of the forecast function is a Forecast object.

The Forecast object includes two variables: median and quantiles, corresponding to different certainty levels in the predictions. These help you understand the range of possible outcomes, from conservative to optimistic.

You can also visualize the forecasts directly by calling the plot function:

forecast.plot()

This quickly generates a chart showing the forecast ranges, making it easy to spot trends and variability in the results. Perfect for a fast, clear analysis.

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Validate forecast accuracy

The evaluate function measures the performance of a time series forecasting model by sampling random time windows from the input dataset, generating forecasts, and calculating forecast errors. Designed for robustness, it supports evaluation across single or multiple time series and computes metrics like Weighted Quantile Loss (WQL) or Weighted Absolute Percentage Error (WAPE). By running multiple iterations, it ensures reliable aggregation of results, providing insights into the model’s accuracy and adaptability to various time windows. This function is ideal for validating forecasting models in scenarios with seasonality, trends, or diverse time series data.

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Parameters