Timecopilot
Open-source forecasting agent that uses an LLM to pick, run, and explain time series foundation models in one command.
TimeCopilot collapses model selection, validation, and the write-up into one command, and the GIFT-Eval #1 placement plus NeurIPS 2025 acceptance give that claim teeth. It is free and open-source, so your real bill is compute and LLM tokens rather than a license. If you would otherwise be gluing Chronos and TimesFM together by hand, this saves you the glue.
Verified 4d ago · liveness 56/100 · cite: rightaichoice.com/tools/timecopilot
- Data scientists who want an explainable forecast from a CSV without building a pipeline
- Developers automating forecasting in Python or CI with the SDK and CLI
- Analysts who would rather ask a question in plain English than write model-selection code
- Researchers comparing, fine-tuning, and ensembling time series foundation models
- Users who need a GUI or visual dashboard — this is CLI and Python only
- Real-time or high-frequency streaming forecasts such as tick data or live IoT feeds
- Non-technical stakeholders who will not touch a terminal or Python
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip TimeCopilot if you need a browser dashboard or real-time streaming forecasts, or if you are stuck on Python below 3.10 or an Intel Mac where the PyTorch install fails.
Every agentic run calls an LLM, so each forecast consumes OpenAI (or Bedrock/Google) tokens on top of the free library
TimeCopilot is free and open-source, so the spend comparison is not against a paid seat but against the two things you supply: LLM tokens per agent run and the compute for fine-tuning or large ensembles. That makes it cheaper than a hosted forecasting API with per-series fees when you already have Python and cloud credits, and more expensive in time than nothing at all if you would have been happy with a single ARIMA fit.
In short
Timecopilot — Open-source forecasting agent that uses an LLM to pick, run, and explain time series foundation models in one command. Best for Data scientists who want an explainable forecast from a CSV without building a pipeline, Developers automating forecasting in Python or CI with the SDK and CLI, Analysts who would rather ask a question in plain English than write model-selection code. Free to use.
What people actually say about Timecopilot — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
2 mentions across 1 source (Hacker News) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Natural-language queries make forecasting accessible to non-experts.
- +Unified API over 30+ foundation models simplifies experimentation.
- +LLM-driven explanations help communicate results to stakeholders.
- +Single command line forecast from a URL speeds up prototyping.
- +Automatic cross-validation and anomaly detection built in.
- −Requires multiple API keys, adding setup overhead.
- −Real-world reliability and production readiness unproven.
- −Installation can be tricky; docs need improvement.
- −LLM model selection may be unreliable on some datasets.
- −Dependency on external model APIs creates vendor risk.
- • LLM API usage costs (e.g., OpenAI tokens) are not included.
- • Foundation model API calls may have separate pricing from providers.
Viability Score
How well maintained and how widely used is Timecopilot? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Unified API over 30+ time series foundation models (Chronos, Moirai, TimesFM, TimeGPT, TiRex, TabPFN, Toto)
- LLM-driven automated model selection from tsfeatures and series characteristics
- Natural-language forecasting: ask a plain-English question and get a forecast plus analysis
- Cross-validation across candidate models before a winner is chosen
- Comparison against a seasonal naive baseline before accepting a forecast
- LLM-written explanations of model choice and forecast interpretation
- One-line CLI forecasting: uvx timecopilot forecast <url>
- Python SDK installable with pip install timecopilot
- Configurable LLM per run, including non-OpenAI endpoints
- Routing LLM calls through AWS Bedrock or Google endpoints
- Async client with streaming query responses
- Anomaly detection workflow via the TimeCopilot forecaster
- Forecasting, anomaly detection, visualization, and combined analysis modes
- Foundation model ensembling and GIFT-Eval custom ensembles
- Fine-tuning of time series foundation models
About Timecopilot
TimeCopilot is an open-source forecasting agent that puts a large language model in charge of the boring half of time series work: reading the data, choosing a model, validating it, and writing up why. You hand it a dataset, not a pipeline. The agent interprets statistical features, selects from 30+ time series foundation models including Amazon Chronos, Salesforce Moirai, Google TimesFM, and Nixtla TimeGPT, runs cross-validation, compares candidates against a seasonal naive baseline, and returns an explanation alongside the numbers. The fastest entry point is one line: uvx timecopilot forecast <url> pulls a public dataset from the web and returns a forecast in seconds, with no local files or script. Flags swap the LLM (default openai:gpt-4o-mini, or openai:gpt-4o, or a non-OpenAI endpoint) and let you ask a plain-English question such as how many air passengers to expect over the next 12 months. For anything programmatic, pip install timecopilot exposes the same agent as TimeCopilot(llm=..., retries=...), with forecast and streaming query methods and an async variant. Outputs are structured rather than a bare array of numbers: tsfeatures results and a natural-language analysis of them, the selected model plus its technical details, cross-validation results, a comparison explaining why some models did better, a flag for whether the winner beats seasonal naive, and an interpretation of the forecast. Beyond a single model the docs cover fine-tuning foundation models, building custom ensembles through GIFT-Eval, and sktime integration for 200+ classical and neural forecasters including ARIMA, BATS, VAR and Prophet. Per-family guides walk through Chronos, TimesFM, PatchTST-FM, T0, TiRex, TabPFN and Toto. TimeCopilot reached #1 on the GIFT-Eval benchmark above AWS, Salesforce, Google, IBM and top universities, and its agentic-forecasting paper was accepted at the NeurIPS 2025 BERTs Workshop. There is no GUI and no dashboard, so the honest comparison is against
Behind the Verdict
We would reach for TimeCopilot when the forecast has to be defensible, not just produced. The agent picks a model from tsfeatures, cross-validates the candidates, checks the winner against seasonal naive, and hands you a written reason for the choice. That audit trail is the part most teams skip and later regret. Pick it if you live in Python or a terminal and want an explainable forecast from a CSV without building a pipeline. Pick it if you are already torn between Chronos, TimesFM and TimeGPT and want several of them behind one interface, or if you need to route LLM calls through AWS Bedrock or Google endpoints for compliance reasons. Pass if anyone on the team needs a visual dashboard. There is a CLI and a Python API, nothing else. Pass on tick data and live IoT feeds, where the batch forecast loop is the wrong shape. Pass if you will never touch a terminal or Python, and check your setup first: Python 3.10+ is required and macOS on Intel (x86_64) is a known install headache with dependencies like PyTorch. The closest alternative is a hosted forecasting API such as Nixtla TimeGPT. That buys you a managed endpoint and support; TimeCopilot buys you model choice and an explanation layer, with the LLM token cost and ensemble compute on your side. The other alternative is your own script around one foundation model. That works until you need to compare candidates or justify the pick. In practice, budget for LLM tokens per run, especially when you let the agent chat through a large comparison. Heavy ensembles carry real compute cost too. Start with the one-line CLI on a sample series before wiring the SDK into CI, and keep the cross-validation output in your logs. That is the artifact reviewers will ask for.
Researching Timecopilot? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Timecopilot actually fits — and what changes day-one when you adopt it.
Run uvx timecopilot forecast on the hosted CSV URL with --llm openai:gpt-4o, then read the tsfeatures summary, the selected model, the cross-validation table, and the written interpretation.
Outcome: A defensible forecast plus a rationale you can paste into a planning doc, produced in one command rather than a notebook of model-selection code.
pip install timecopilot, initialize TimeCopilot(llm=..., forecasters=...) inside the service, call analyze and forecast on incoming series, and use AsyncTimeCopilot with query_stream for the API layer.
Outcome: Forecasting runs as a library call in your existing stack, with LLM traffic routed through AWS Bedrock if your cloud commitment requires it.
Build a custom ensemble across Chronos, TimesFM and Moirai, compare it with the GIFT-Eval ensemble guide, and inspect the produced forecast against the seasonal naive baseline.
Outcome: A reproducible ensemble configuration with cross-validation numbers you can cite, and per-family guides for swapping in TiRex, TabPFN or Toto.
Use Cases
- Forecast monthly sales from a CSV URL with a single uvx command and get a written rationale for the model chosen
- Compare and ensemble Chronos, Moirai, and TimesFM forecasts for the same dataset before committing
- Detect anomalies in energy consumption series using the built-in anomaly detection workflow
- Fine-tune a foundation model on custom cryptocurrency price data
- Run cross-validation across 10+ models to select the best forecaster for a business metric
- Ask 'How many air passengers are expected in total in the next 12 months?' and get a direct answer plus interpretation
- Route agent LLM calls through AWS Bedrock so forecasting traffic stays inside your cloud perimeter
- Swap in sktime classical models like ARIMA or Prophet when a foundation model is not the right fit
Models Under the Hood
as of 2026-10-16
Limitations
- TimeCopilot runs through the CLI or the Python SDK, so non-technical stakeholders cannot self-serve through a GUI.
- Agentic features depend on an LLM provider — the Python example configures "openai:gpt-4o" — so each run consumes tokens and needs credentials.
- Model selection is evidence-bound to the demonstrated Chronos-2 vs.
- TimesFM comparison and a seasonal-naive baseline check; the GIFT-Eval #1 placement is a benchmark result, not a guarantee on your own series.
- Foundation-model work (ensembling, fine-tuning) requires additional compute beyond a laptop.
as of 2026-10-03
Verification history
We have re-verified Timecopilot 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Timecopilot's pricing actually pencils out — and where peers do it cheaper.
TimeCopilot is free and open-source, so the spend comparison is not against a paid seat but against the two things you supply: LLM tokens per agent run and the compute for fine-tuning or large ensembles. That makes it cheaper than a hosted forecasting API with per-series fees when you already have Python and cloud credits, and more expensive in time than nothing at all if you would have been happy with a single ARIMA fit.
Setup time & first value
How long it actually takes to get something useful out of Timecopilot — broken out by persona, not the marketing-page minute.
Fastest path: with Python 3.10+ and an OPENAI_API_KEY exported, uvx timecopilot forecast <csv-url> returns a first forecast in about a minute. The SDK route is pip install timecopilot plus the same key, then a TimeCopilot(llm=...) call — figure 10-20 minutes to a working script. Routing through AWS Bedrock or Google endpoints and any fine-tuning or ensemble work are the slow parts, not the
Switching to or from Timecopilot
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hand-rolled Chronos or TimesFM script: re-point the same CSV at uvx timecopilot forecast and keep your old script as the cross-validation reference.
- →From Nixtla TimeGPT API calls: wrap TimeGPT as one of the forecasters in TimeCopilot(llm=..., forecasters=[...]) and let the agent compare it against Chronos and TimesFM.
- →From a Prophet-only pipeline: bring Prophet in through the sktime integration so it competes in the same cross-validation run as the foundation models.
- ↗To a hosted forecasting API: hand the same series to a managed endpoint when you no longer want to run models or LLM calls yourself.
- ↗To a plain sktime or StatsForecast pipeline: keep the classical models TimeCopilot selected and drop the agent layer once model choice is settled.
- ↗To a BI dashboard tool: export the TimeCopilot forecast and interpretation into your reporting layer when stakeholders need charts rather than a terminal.
Integrations
Resources & Guides
- Resourcetimecopilot.dev
Home · Timecopilot
Helpful link from timecopilot.dev
- Quickstarttimecopilot.dev
Quickstart · Timecopilot
Get up and running fast from timecopilot.dev
- Quickstarttimecopilot.dev
Installation · Timecopilot
Get up and running fast from timecopilot.dev
- API Referencetimecopilot.dev
Agent · Timecopilot
Methods, params, types from timecopilot.dev
- Resourcetimecopilot.dev
Changelogs · Timecopilot
Helpful link from timecopilot.dev
Tutorials & Learning
YouTube returned 6 videos for “Timecopilot”, and we withheld 6: 6 could not be judged, because “Timecopilot” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Timecopilot.
Official links
Tools that pair well with Timecopilot
Common stack mates teams adopt alongside Timecopilot, with the specific reason each pairing earns its keep.
OpenAgents
OpenAgents is an open-source language agent platform for data analysis, 200+ plugins and autonomous web browsing.
Clay
Clay is a GTM data enrichment and AI agent workspace that builds custom outbound systems on 200+ data sources
Dcipher Insight Booster
Agentic AI research workflows that turn multi-source text into source-linked, audit-ready reports at enterprise scale.
Featured Head-to-Head Comparisons
Timecopilot vs Screenplayiq
ScreenplayIQ and TimeCopilot serve completely different domains. ScreenplayIQ is a specialized tool for screenwriters and producers needing script marketability analysis and box office predictions. TimeCopilot is an open-source forecasting agent for data scientists and developers who want quick time series forecasts via natural language or API. Your choice depends entirely on whether you analyze screenplays or data. For forecasting, TimeCopilot's free open-source model with 230+ models (via new sktime integration) is hard to beat. For screenwriting, ScreenplayIQ offers unique financial insights not found in generic script coverage tools.
Timecopilot vs Geologicai
Choose GeologicAI if you are a mining company needing rapid, integrated core scanning and AI modeling for critical minerals; choose Timecopilot if you are a data scientist or developer needing a free, open-source forecasting tool with natural-language support. They serve completely different domains — GeologicAI is domain-specific enterprise software, Timecopilot is a general-purpose forecasting utility.
Timecopilot vs Nectar Energy
Choose Nectar Energy if you manage commercial building energy systems and need turnkey HVAC/lighting automation plus ESG reporting. Choose Timecopilot if you're a data scientist/developer needing flexible, code-based forecasting from CSV data with LLM-driven model selection—at zero cost.
Alternatives to Timecopilot
View all
OpenAgents
OpenAgents is an open-source language agent platform for data analysis, 200+ plugins and autonomous web browsing.
Clay
Clay is a GTM data enrichment and AI agent workspace that builds custom outbound systems on 200+ data sources
Dcipher Insight Booster
Agentic AI research workflows that turn multi-source text into source-linked, audit-ready reports at enterprise scale.
Frequently Asked Questions
Best-of guides
Used Timecopilot? Help shape our editorial sentiment research.