Types

Core data types and result objects used throughout Vectrix.

ForecastResult

Main result from Vectrix.forecast(). Not the same as EasyForecastResult from the Easy API.

Field	Type	Description
`success`	`bool`	Whether forecast succeeded
`predictions`	`np.ndarray`	Forecast values
`dates`	`list[str]`	Forecast dates
`lower95`	`np.ndarray`	95% lower bound
`upper95`	`np.ndarray`	95% upper bound
`bestModelId`	`str`	Selected model ID
`bestModelName`	`str`	Model display name
`allModelResults`	`dict[str, ModelResult]`	All model results
`characteristics`	`DataCharacteristics`	Data properties

!!! note “EasyForecastResult vs ForecastResult” The Easy API returns EasyForecastResult with .lower / .upper. The Vectrix class returns ForecastResult with .lower95 / .upper95.

ModelResult

Per-model result stored in ForecastResult.allModelResults.

Field	Type	Description
`modelId`	`str`	Model identifier
`modelName`	`str`	Display name
`isValid`	`bool`	Whether model produced valid output
`mape`	`float`	Validation MAPE
`rmse`	`float`	Validation RMSE
`mae`	`float`	Validation MAE
`smape`	`float`	Validation sMAPE
`predictions`	`np.ndarray`	Model predictions
`lower95`	`np.ndarray`	Lower bound
`upper95`	`np.ndarray`	Upper bound
`trainingTime`	`float`	Training time (seconds)
`flatInfo`	`FlatPredictionInfo`	Flat detection info

DataCharacteristics

Field	Type	Description
`length`	`int`	Number of observations
`period`	`int`	Detected seasonal period
`frequency`	`str`	Frequency label (D, W, M, etc.)
`hasTrend`	`bool`	Whether trend detected
`trendDirection`	`str`	‘increasing’, ‘decreasing’, ‘none’
`trendStrength`	`float`	0–1 strength
`hasSeasonality`	`bool`	Whether seasonality detected
`seasonalStrength`	`float`	0–1 strength
`predictabilityScore`	`float`	0–100 score

DNAProfile

Returned by analyze().dna or ForecastDNA().analyze().

Field	Type	Description
`features`	`dict[str, float]`	65+ statistical features
`fingerprint`	`str`	8-char hex hash
`difficulty`	`str`	‘easy’, ‘medium’, ‘hard’, ‘very_hard’
`difficultyScore`	`float`	0–100 score
`recommendedModels`	`list[str]`	Sorted by fitness
`category`	`str`	‘trending’, ‘seasonal’, ‘stationary’, etc.
`summary`	`str`	Natural language summary

!!! warning “Feature values are inside the features dict”

# CORRECT
dna.features['trendStrength']
dna.features['seasonalStrength']
dna.features['hurstExponent']

# WRONG — AttributeError
dna.trendStrength
dna.seasonalStrength
```

**Key feature names:** `trendStrength`, `seasonalStrength`, `seasonalPeakPeriod`, `hurstExponent`, `volatility`, `cv`, `skewness`, `kurtosis`, `adfStatistic`, `spectralEntropy`, `approximateEntropy`, `garchEffect`, `volatilityClustering`, `demandDensity`, `nonlinearAutocorr`, `forecastability`, `trendSlope`, `trendDirection`, `trendLinearity`, `trendCurvature`

## ModelInfo

Available model catalog entry.

| Field | Type | Description |
|---|---|---|
| `id` | `str` | Model identifier |
| `name` | `str` | Display name |
| `category` | `str` | Model category |
| `description` | `str` | Brief description |
| `strengths` | `list[str]` | What the model is good at |