How to Use PyCausalSim to Improve LNG and Natural Gas Forecasting
Machine learning could not beat the NYMEX natural gas futures curve. A causal simulation built on it can still improve the decisions that ride on Henry Hub:…
Practice area · Kansas City and remote
Forecasting & Simulation
A forecast is only useful if you know how wrong it is likely to be. Vector 1 builds forecasts and simulations for demand, commodities, energy and capacity, tests them against simple benchmarks such as the futures curve, and turns the uncertainty into decisions: ranges, scenarios and plans that hold up when the point forecast misses.
What we do
- Demand, capacity and commodity forecasting with backtests against naive and market benchmarks
- Monte Carlo and causal simulation for scenario planning and stress tests
- Commodity and LNG economics: futures curves, shipping costs and netbacks
- Test and pilot sizing: how much data you need before a result means anything
Talk about your project
Tell us what you're trying to measure, forecast or decide. We work with teams in Kansas City and remotely, and we reply within two business days.
Email [email protected]Try it: interactive demos
LNG Voyage Economics
Where should a US Gulf LNG cargo go? Charter days, boil-off, canal fees and the netback to Europe or Asia, by route, with your own prices.
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Run a simulated SEO split test, read the lift with an honest confidence interval, and find out how many pages and weeks you need before a real change is detectable.
Open demo →Where AI Pays Off First (Kansas City)
How many months until you can prove an AI use case worked? Sixteen use cases across eight Kansas City industries, with every input editable, plus payback once you count captured savings.
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All 34 articles on this topic →
PyCausalSim 0.3.0: Causal Simulation for Monthly Forecasts, Resampled History and Honest Backtests
An open-source Python library for causal inference and simulation, now with whole-episode resampling, window claims and rolling backtests, built and tested on…
Your Best-Looking Ad Channel May Be Doing the Least: A CMO’s Guide to Marketing Mix Modeling
In a two-year simulation, last-click attribution said branded search returned $13.26 per dollar. The true number was $1.20. What the evidence says, what a…
There Is Plenty of Power for AI. The Shortage Story Is Misinformed or Self-Serving.
Inflated forecasts, outdated per-query myths and a market that pays incumbents for scarcity have turned a pricing problem into a supply panic. A model built on…
Your AI Metric Is Probably Lying to You
Four new papers, one simulation, and a test for knowing when a proxy stops meaning anything Autonomous coding agents raise commit volume by 240 percent. They…
A Causal Inference Guide to the AI and Energy Feedback Loop
Everything here is reproducible. Paid subscribers to Vector1 Research get the complete code, the simulated AI and energy models, and all figures at…
Modeling Crop Patterns and Yields Under Input and Climate Shocks
Everything here is reproducible. Paid subscribers get the complete code, the simulated crop and supply models, and all figures:…
Simulating the AI Structural Shift: Measuring the Human Impact with PyCausalSim
A fifteen-year forecast, encoded as a temporal causal model and run 4,000 times. One policy lever turns out to be worth 2.8 of another, and the ranking…
AI Models Aren’t Valuable. They’re a Commodity, and Investors Should Be Wary.
Open-weight models, many from Chinese labs, now trail the closed frontier by a matter of months and cost a fraction as much to use. For investors, this…
Common questions
Can machine learning beat the futures curve?
Often not. Futures prices already reflect the market's information, so they are a hard benchmark. In Vector 1's natural gas backtests, machine learning lost to the futures curve at one- to four-month horizons. The value of modeling usually comes from ranges, scenarios and decisions against a risk limit, not from beating the curve.
What is Monte Carlo simulation used for?
Monte Carlo simulation runs a model thousands of times with uncertain inputs drawn from realistic distributions, producing a range of outcomes instead of one number. It is used for risk, capacity planning, budgeting and for checking whether a test or pilot is large enough to detect the effect you care about.
Do you work with teams outside Kansas City?
Yes. Vector 1 is based in Kansas City and works with teams in the metro and remotely.