Chronos: Forecast Any Time Series Without Training a Model
Plus an AI agent built for data engineers
Grab your coffee. Here are this week’s highlights.
🤝 COLLABORATION
Give Your AI Agent Live Web Access with Bright Data MCP
With basic search APIs, agents often miss critical context from sources like social platforms, forums, news, and answer engines. That leads to incomplete or outdated responses.
Bright Data’s MCP server unifies all web data access into one interface your AI agent can use directly.
With Bright Data MCP, your AI agent can access:
Search engines (Google, Bing, more)
Social media (Twitter/X, Reddit, Instagram, TikTok)
Web archives (historical web data, years deep)
Answer engines (ChatGPT, Perplexity, Gemini)
All through one connection.
📅 Today’s Picks
Chronos: Forecast Any Time Series Without Training a Model
Problem
Traditional forecasting requires domain-specific data, feature engineering, and multiple rounds of model tuning.
Solution
Chronos is a family of pretrained time series forecasting models from Amazon Science that deliver zero-shot predictions out of the box.
Simply load a pretrained model and generate forecasts on any time series data, with no fine-tuning required.
If zero-shot accuracy isn’t enough, you can fine-tune on your data with AutoGluon in a few lines.
🧪 Run code
altimate-code: The Missing AI Layer for Data Engineering Teams
Problem
General AI tools can write SQL and catch obvious mistakes. But they cannot systematically detect anti-patterns, trace lineage, or keep warehouse costs under control.
That gap can lead to inefficient queries, broken dependencies, and hidden compliance risks building up over time.
Solution
I recently tried altimate-code, an open-source agent with 100+ tools purpose-built for data engineers, and built a demo repo to test it.
From a single prompt, it generated a full dbt project with staging, intermediate, and mart layers, added automated tests, and built an interactive dashboard.
What makes it different:
100+ tools that analyze SQL through structural parsing, not text guessing
Works across your stack including Snowflake, BigQuery, Databricks, DuckDB, and more
Model-agnostic. Compatible with OpenAI, Anthropic, Gemini, Ollama, and others
☕️ Weekly Finds
timesfm [Machine Learning] - Pretrained time series foundation model by Google Research for zero-shot forecasting
darts [Machine Learning] - A Python library for user-friendly forecasting and anomaly detection on time series
orbit [Machine Learning] - A Python package for Bayesian time series forecasting with probabilistic models under the hood
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