Time Series Forecasting
Made Simple
One line of code. Automatic model selection. Zero configuration.
Built-in Rust engine — Python syntax, Rust speed.
result = forecast("sales.csv", steps=12)
result.plot() # auto model + intervals + plot
Everything you need for forecasting
From one-line forecasts to advanced adaptive intelligence — Vectrix scales with your needs.
Zero Configuration
Pass your data, get forecasts. Auto model selection, hyperparameter tuning, and flat-line defense — all automatic.
Automatic Model Selection
Evaluates 30+ models (ETS, ARIMA, Theta, MSTL, DOT, CES, 4Theta, ESN, DTSF) and picks the best one for your data.
Flat Defense System
Unique 4-level system prevents constant predictions — the most common forecasting failure mode in production.
Forecast DNA
65+ feature fingerprinting. Know your data's difficulty, optimal model, seasonality, and similarity to other series.
Adaptive Intelligence
Regime detection (HMM), self-healing forecasts (CUSUM + EWMA), and 8 business constraint types.
Built-in Rust Engine
Pre-compiled Rust engine included in every wheel — like Polars. 29 accelerated hot loops, up to 67x faster.
Probabilistic Forecasting
Parametric distributions (Gaussian, Student-t, Log-Normal), quantile forecasts, conformal prediction intervals, CRPS.
R-Style Regression
Formula interface: regress(df, "sales ~ ads + price"). OLS, Ridge, Lasso, Huber, Quantile with full diagnostics.
Business Intelligence
Anomaly detection, what-if scenarios, backtesting framework, and production metrics (WAPE, MASE, bias).
Three lines. Full pipeline.
Forecasting, analysis, and regression — each designed to work with one function call.
result = forecast("sales.csv", steps=12)
result.plot()
result.to_csv("forecast.csv")
report = analyze("data.csv")
print(report.dna.difficulty)
print(report.dna.recommended_model)
r = regress(df, "sales ~ ads + price")
print(r.r_squared)
r.plot()
30+ models, one unified API
Every model shares the same fit → predict interface. Vectrix picks the best one for your data automatically.
Exponential Smoothing
ARIMA Family
Decomposition
Non-Parametric
Volatility & Special
ML & Foundation
Forecasts that adapt to reality
Real-world data is messy. Regimes shift, anomalies appear, business rules matter. Vectrix handles it all.
HMM-based automatic regime identification. Detect structural breaks and adapt model selection accordingly.
CUSUM + EWMA monitoring. When predictions drift, the system automatically corrects before errors accumulate.
Non-negative, bounded range, monotonic, integer-only, seasonal floor/ceiling, growth rate limits, and more.
65+ features: trend strength, seasonality type, noise ratio, Hurst exponent, demand density, and more.
vx = Vectrix(data)
# regime detection
regimes = vx.detectRegimes()
# self-healing forecast
healed = vx.healForecast(
steps=12,
actual=newData
)
# business constraints
result = vx.forecast(
steps=12,
constraints=[
NonNegative(),
MaxGrowth(0.1)
]
)
Built-in Rust Engine
29 Rust-accelerated core loops compiled into every wheel. Like Polars — pip install and it's fast.
pip install vectrix — Rust engine included in every wheel. No extras, no flags needed.
M3 & M4 Competition Results
Tested against 100,000+ real-world time series. OWA < 1.0 means beating the Naive2 baseline. Lower is better.
| Competition | Yearly | Quarterly | Monthly | Weekly | Daily | Hourly |
|---|---|---|---|---|---|---|
| M3 | 0.848 | 0.825 | 0.758 | — | — | 0.819 |
| M4 DOT-Hybrid | 0.797 | 0.894 | 0.897 | 0.959 | 0.820 | 0.722 |
| M4 VX-Ensemble | 0.879 | 0.907 | 0.919 | 0.954 | 0.820 | 0.696 |
DOT-Hybrid: single model, AVG OWA 0.848. VX-Ensemble: DOT + AutoCES + 4Theta. Hourly 0.696 = competition winner level. Daily 0.820 = strong Naive2 outperformance.
View full benchmark results →How Vectrix compares
Purpose-built for production forecasting with features no other library offers.
| Capability | Vectrix | statsforecast | Prophet | Darts |
|---|---|---|---|---|
| Zero-config forecasting | ||||
| Built-in Rust engine (5-67x) | ||||
| 30+ statistical models | ||||
| Flat prediction defense | ||||
| Forecast DNA fingerprinting | ||||
| Business constraints (8 types) | ||||
| R-style regression | ||||
| Self-healing forecasts | ||||
| Foundation model wrappers | ||||
| Interactive visualization |
Built for real-world applications
From demand planning to financial modeling — Vectrix adapts to your domain.
Retail & E-Commerce
Demand forecasting, inventory optimization, seasonal planning with intermittent demand support (Croston, ADIDA).
Finance & Trading
Volatility modeling (GARCH), regime detection for market states, probabilistic risk assessment with confidence intervals.
Energy & Utilities
Load forecasting with multi-seasonal patterns (hourly/daily/weekly). MSTL and TBATS handle complex seasonality.
Healthcare & IoT
Patient volume prediction, sensor data forecasting, anomaly detection with self-healing for drift correction.
HR & Workforce
Staffing demand prediction, attrition forecasting. Hierarchical reconciliation for department-level planning.
SaaS & Product
User growth modeling, revenue forecasting, churn prediction. Business constraints enforce realistic growth bounds.
Install in seconds
Rust engine included in every wheel — no extras needed.
Ready to forecast?
Get started with Vectrix in under a minute.