Easy API

The simplest way to use Vectrix. One function call for each task.

Functions

`forecast()`

forecast(
    data,                    # str | DataFrame | ndarray | list | Series | dict
    date=None,               # str — date column name
    value=None,              # str — value column name
    steps=30,                # int — forecast horizon
    verbose=False,           # bool
    models=None,             # list[str] | None — model IDs to evaluate
    ensemble=None,           # str | None — 'mean', 'weighted', 'median', 'best'
    confidence=0.95          # float — 0.80, 0.90, 0.95, 0.99
) -> EasyForecastResult

Available model IDs: 'dot', 'auto_ets', 'auto_arima', 'auto_ces', 'four_theta', 'auto_mstl', 'tbats', 'theta', 'dtsf', 'esn', 'garch', 'croston', 'ets_aan', 'ets_aaa', 'naive', 'mean', 'rwd', 'window_avg', 'egarch', 'gjr_garch', 'seasonal_naive', 'mstl'

`analyze()`

analyze(
    data,                    # str | DataFrame | ndarray | list | Series | dict
    date=None,               # str
    value=None,              # str
    period=None,             # int | None — seasonal period (auto if None)
    features=True,           # bool
    changepoints=True,       # bool
    anomalies=True,          # bool
    anomalyThreshold=3.0     # float — z-score threshold
) -> EasyAnalysisResult

`regress()`

regress(
    y=None,                  # ndarray | Series | None (direct mode)
    X=None,                  # ndarray | DataFrame | None (direct mode)
    data=None,               # DataFrame | None (formula mode)
    formula=None,            # str | None — "y ~ x1 + x2"
    method='ols',            # str — 'ols', 'ridge', 'lasso', 'huber', 'quantile'
    summary=True,            # bool — auto-print summary
    alpha=None,              # float | None — regularization strength
    diagnostics=False        # bool — auto-run diagnostics
) -> EasyRegressionResult

`compare()`

compare(
    data,                    # str | DataFrame | ndarray | list | Series | dict
    date=None,               # str
    value=None,              # str
    steps=30,                # int
    verbose=False,           # bool
    models=None              # list[str] | None
) -> pd.DataFrame           # Returns DataFrame directly, NOT a Result object

Returned DataFrame columns: model, mape, rmse, mae, smape, time_ms, selected

`quickReport()`

quickReport(
    data, date=None, value=None, steps=30
) -> dict                   # Returns dict, NOT a Result object

Returned dict keys: 'forecast' (EasyForecastResult), 'analysis' (EasyAnalysisResult), 'summary' (str)

Alias: quick_report = quickReport (backward compatibility)

`loadSample()`

loadSample(name: str) -> pd.DataFrame

Load a built-in sample dataset.

Available samples: 'airline', 'retail', 'stock', 'temperature', 'energy', 'web', 'intermittent'

Sample	date col	value col
airline	date	passengers
retail	date	sales
stock	date	close
temperature	date	temperature
energy	date	consumption_kwh
web	date	pageviews
intermittent	date	demand

`listSamples()`

listSamples() -> pd.DataFrame

List available built-in sample datasets.

Result Classes

EasyForecastResult

Attributes:

Attribute	Type	Description
`.predictions`	`np.ndarray`	Forecast values
`.dates`	`list[str]`	Forecast dates
`.lower`	`np.ndarray`	Lower CI
`.upper`	`np.ndarray`	Upper CI
`.model`	`str`	Best model name
`.mape`	`float`	MAPE %
`.rmse`	`float`	RMSE
`.mae`	`float`	MAE
`.smape`	`float`	sMAPE
`.models`	`list[str]`	All valid model names (sorted by MAPE)

Methods:

Method	Alias	Returns	Description
`summary()`	—	`str`	Text summary
`toDataframe()`	`to_dataframe()`	`DataFrame`	date, prediction, lower95, upper95
`compare()`	—	`DataFrame`	All models ranked by MAPE
`allForecasts()`	`all_forecasts()`	`DataFrame`	date + one col per model
`describe()`	—	`DataFrame`	`.describe()` style stats
`toCsv(path)`	`to_csv(path)`	`self`	Save to CSV
`toJson(path=None)`	`to_json(path=None)`	`str`	JSON string or save to file
`save(path)`	—	`self`	Alias for `toJson(path)`
`plot()`	—	`Figure`	matplotlib plot (optional dep)

EasyAnalysisResult

Attribute	Type	Description
`.dna`	`DNAProfile`	DNA profile object
`.changepoints`	`np.ndarray`	Changepoint int indices (NOT dicts)
`.anomalies`	`np.ndarray`	Anomaly int indices (NOT dicts)
`.features`	`dict`	Statistical features dict
`.characteristics`	`DataCharacteristics`	Data characteristics
`.summary()`	`str`	Formatted report

!!! warning “anomalies/changepoints are int arrays”

# CORRECT
for idx in analysis.anomalies:
    print(f"Anomaly at index {idx}")

# WRONG — will crash
for a in analysis.anomalies:
    print(a['index'], a['value'])  # TypeError!
```

### EasyRegressionResult

**Attributes (camelCase primary, snake_case aliases):**

| Primary | Alias | Type | Description |
|---------|-------|------|-------------|
| `coefficients` | — | `np.ndarray` | Including intercept |
| `pvalues` | — | `np.ndarray` | P-values |
| `rSquared` | `r_squared` | `float` | R² |
| `adjRSquared` | `adj_r_squared` | `float` | Adjusted R² |
| `fStat` | `f_stat` | `float` | F-statistic |
| `durbinWatson` | `durbin_watson` | `float` | Durbin-Watson statistic |

**Methods:**

| Method | Returns | Description |
|--------|---------|-------------|
| `summary()` | `str` | Regression summary table |
| `diagnose()` | `str` | Full diagnostics report |
| `predict(X, interval, alpha)` | `DataFrame` | Predictions with intervals |