Quickstart

Get your first forecast in 3 lines of Python. No configuration, no model selection, no parameter tuning — Vectrix handles everything automatically.

Forecast from a List

The simplest possible usage. Pass any numeric sequence and the number of steps to predict

from vectrix import forecast

result = forecast([100, 120, 130, 115, 140, 160, 150, 170], steps=5)
print(result.model)          # Selected model name
print(result.predictions)    # Forecast values
print(result.summary())      # Full text summary

Behind the scenes, Vectrix evaluates 30+ model candidates, validates each on a holdout set, and returns the winner with 95% confidence intervals.

Forecast from a DataFrame

For real-world data with timestamps, pass a pandas DataFrame. Vectrix auto-detects date and value columns

import pandas as pd
from vectrix import forecast

df = pd.read_csv("sales.csv")
result = forecast(df, date="date", value="sales", steps=30)
result.plot()
result.toCsv("forecast.csv")

Forecast from a CSV File

Skip the pandas step entirely — pass a file path and Vectrix reads it for you

from vectrix import forecast

result = forecast("sales.csv", steps=12)

Working with Results

Every forecast returns an EasyForecastResult object with predictions, confidence intervals, metrics, and export methods

Attribute / Method	Description
`.predictions`	Forecast values (numpy array)
`.dates`	Forecast dates
`.lower`	Lower CI bound
`.upper`	Upper CI bound
`.model`	Selected model name
`.mape`	Validation MAPE (%)
`.rmse`	Validation RMSE
`.smape`	Validation sMAPE
`.summary()`	Formatted text report
`.compare()`	All models ranked by accuracy
`.toDataframe()`	Convert to DataFrame
`.allForecasts()`	Every model’s predictions side-by-side
`.toCsv(path)`	Export to CSV
`.toJson()`	Export to JSON string
`.plot()`	Matplotlib visualization

snake_case aliases (to_dataframe(), all_forecasts(), to_csv(), to_json()) are also available.

Supported Input Formats

Vectrix accepts five input formats — no manual conversion needed

forecast([1, 2, 3, 4, 5])                    # list
forecast(np.array([1, 2, 3, 4, 5]))          # numpy array
forecast(pd.Series([1, 2, 3, 4, 5]))         # pandas Series
forecast(df, date="date", value="sales")      # DataFrame
forecast("data.csv")                           # CSV file path

Quick Analysis

Profile your data before forecasting — understand its difficulty, seasonality, and recommended models

from vectrix import analyze

report = analyze(df, date="date", value="sales")
print(f"Difficulty: {report.dna.difficulty}")
print(f"Category: {report.dna.category}")
print(report.summary())

Quick Regression

R-style regression with automatic diagnostics

from vectrix import regress

model = regress(data=df, formula="sales ~ ads + price")
print(model.summary())
print(model.diagnose())

What’s Next?

Forecasting Guide — Full parameter reference and model categories
Analysis & DNA — Understand your data’s DNA fingerprint
API Reference — Complete Easy API specification