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Dominik Safaric
Dominik Safaric

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Forecast anything without the ML hustle.

Getting accurate univariate forecasts shouldn’t require weeks of effort or a degree in machine learning. Whether it’s planning inventory, predicting energy output, or optimizing a supply chain, time series forecasting should be simple, scalable, and reliable.

That’s why we built Sulie: a foundation model designed to make forecasting easier, faster, and accessible to everyone.

🔎 What Is Sulie?

Sulie is a foundation model for time series forecasting. Trained on diverse datasets across industries and tasks, Sulie is industry-agnostic and data-flexible, making it capable of tackling a wide range of time series forecasting problems without requiring task-specific customization.

🥁 Why Sulie?

Here’s why Sulie stands out:

  • Forecast in a Few Lines of Code - start forecasting quickly, without complex pipelines or setup.
  • Auto Fine-Tuning - Sulie adapts to your data to improve accuracy while you stay focused on your goals.
  • Zero ML Hassle - No need to train models, fiddle with hyperparameters or deal with infrastructure. Sulie takes care of the heavy lifting.

🔥 Real-World Use Cases

Sulie is perfect for:

  • Energy - Predict renewable energy output, like wind or solar generation.
  • Retail - Plan product restocking and forecast sales trends.
  • Supply Chain - Optimize logistics and reduce inventory costs.
  • Financial Data - Forecast trends in revenue or expenses.

📚 How It Works

Integrating Sulie into your workflow or SaaS is simple. Here’s an example:

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"
)
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Image of Timescale

🚀 pgai Vectorizer: SQLAlchemy and LiteLLM Make Vector Search Simple

We built pgai Vectorizer to simplify embedding management for AI applications—without needing a separate database or complex infrastructure. Since launch, developers have created over 3,000 vectorizers on Timescale Cloud, with many more self-hosted.

Read more

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