Minor fixes (#1071)

Author: deven367

nbs/docs/contribute/step-by-step.md

@@ -1,6 +1,7 @@
-# Step-by-step Contribution Guide
-
-> This document contains instructions for collaborating on the different libraries of Nixtla.
+---
+title: Step-by-step Contribution Guide
+description: This document contains instructions for collaborating on the different libraries of Nixtla.
+---
 
 Sometimes, diving into a new technology can be challenging and overwhelming. We've been there too, and we're more than ready to assist you with any issues you may encounter while following these steps. Don't hesitate to reach out to us on [Slack](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ). Just give fede a ping, and she'll be glad to assist you.
 

nbs/docs/contribute/techstack.md

@@ -1,4 +1,7 @@
-# Contributing Code to Nixtla Development
+---
+title: Contributing Code to Nixtla Development
+description: A guide on the technical skills and tools needed to contribute code to the Nixtla project.
+---
 
 Curious about the skills required to contribute to the Nixtla project?
 

nbs/src/core/models_intro.qmd

@@ -8,84 +8,84 @@ Automatic forecasting tools search for the best parameters and select the best p
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`AutoARIMA`](../../models.html#autoarima)|✅|✅|✅|✅|✅|
-|[`AutoETS`](../../models.html#autoets)|✅|✅|✅|✅|✅|
-|[`AutoCES`](../../models.html#autoces)|✅|✅|✅|✅||
-|[`AutoTheta`](../../models.html#autotheta)|✅|✅|✅|✅|✅|
+|[`AutoARIMA`](models.html#autoarima)|✅|✅|✅|✅|✅|
+|[`AutoETS`](models.html#autoets)|✅|✅|✅|✅|✅|
+|[`AutoCES`](models.html#autoces)|✅|✅|✅|✅||
+|[`AutoTheta`](models.html#autotheta)|✅|✅|✅|✅|✅|
 
 
 ## ARIMA Family
 These models exploit the existing autocorrelations in the time series.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`ARIMA`](../../models.html#arima)|✅|✅|✅|✅|✅|
-|[`AutoRegressive`](../../models.html#autoregressive)|✅|✅|✅|✅|✅|
+|[`ARIMA`](models.html#arima)|✅|✅|✅|✅|✅|
+|[`AutoRegressive`](models.html#autoregressive)|✅|✅|✅|✅|✅|
 
 
 ## Theta Family
 Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`Theta`](../../models.html#theta)|✅|✅|✅|✅|✅|
-|[`OptimizedTheta`](../../models.html#optimizedtheta)|✅|✅|✅|✅|✅|
-|[`DynamicTheta`](../../models.html#dynamictheta)|✅|✅|✅|✅|✅|
-|[`DynamicOptimizedTheta`](../../models.html#dynamicoptimizedtheta)|✅|✅|✅|✅|✅|
+|[`Theta`](models.html#theta)|✅|✅|✅|✅|✅|
+|[`OptimizedTheta`](models.html#optimizedtheta)|✅|✅|✅|✅|✅|
+|[`DynamicTheta`](models.html#dynamictheta)|✅|✅|✅|✅|✅|
+|[`DynamicOptimizedTheta`](models.html#dynamicoptimizedtheta)|✅|✅|✅|✅|✅|
 
 
 ## Multiple Seasonalities
 Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`MSTL`](../../models.html#mstl)|✅|✅|✅|✅|✅|
+|[`MSTL`](models.html#mstl)|✅|✅|✅|✅|✅|
 
 
 ## GARCH and ARCH Models
 Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH. 
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`GARCH`](../../models.html#garch)|✅|✅|✅|✅|✅|
-|[`ARCH`](../../models.html#arch)|✅|✅|✅|✅|✅|
+|[`GARCH`](models.html#garch)|✅|✅|✅|✅|✅|
+|[`ARCH`](models.html#arch)|✅|✅|✅|✅|✅|
 
 
 ## Baseline Models
 Classical models for establishing baseline.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`HistoricAverage`](../../models.html#historicaverage)|✅|✅|✅|✅|✅|
-|[`Naive`](../../models.html#naive)|✅|✅|✅|✅|✅|
-|[`RandomWalkWithDrift`](../../models.html#randomwalkwithdrift)|✅|✅|✅|✅|✅|
-|[`SeasonalNaive`](../../models.html#seasonalnaive)|✅|✅|✅|✅|✅|
-|[`WindowAverage`](../../models.html#windowaverage)|✅|||||
-|[`SeasonalWindowAverage`](../../models.html#seasonalwindowaverage)|✅|||||
+|[`HistoricAverage`](models.html#historicaverage)|✅|✅|✅|✅|✅|
+|[`Naive`](models.html#naive)|✅|✅|✅|✅|✅|
+|[`RandomWalkWithDrift`](models.html#randomwalkwithdrift)|✅|✅|✅|✅|✅|
+|[`SeasonalNaive`](models.html#seasonalnaive)|✅|✅|✅|✅|✅|
+|[`WindowAverage`](models.html#windowaverage)|✅|||||
+|[`SeasonalWindowAverage`](models.html#seasonalwindowaverage)|✅|||||
 
 
 ## Exponential Smoothing
 Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the `SimpleExponential` family for data with no clear trend or seasonality.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`SimpleExponentialSmoothing`](../../models.html#simpleexponentialsmoothing)|✅|||||
-|[`SimpleExponentialSmoothingOptimized`](../../models.html#simpleexponentialsmoothingoptimized)|✅|||||
-|[`Holt`](../../models.html#holt)|✅|✅|✅|✅|✅|
-|[`HoltWinters`](../../models.html#holtwinters)|✅|✅|✅|✅|✅|
+|[`SimpleExponentialSmoothing`](models.html#simpleexponentialsmoothing)|✅|||||
+|[`SimpleExponentialSmoothingOptimized`](models.html#simpleexponentialsmoothingoptimized)|✅|||||
+|[`Holt`](models.html#holt)|✅|✅|✅|✅|✅|
+|[`HoltWinters`](models.html#holtwinters)|✅|✅|✅|✅|✅|
 
 
 ## Sparse or Intermittent 
 Suited for series with very few non-zero observations
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`ADIDA`](../../models.html#adida)|✅|||||
-|[`CrostonClassic`](../../models.html#crostonclassic)|✅|||||
-|[`CrostonOptimized`](../../models.html#crostonoptimized)|✅|||||
-|[`CrostonSBA`](../../models.html#crostonsba)|✅|||||
-|[`IMAPA`](../../models.html#imapa)|✅|||||
-|[`TSB`](../../models.html#tsb)|✅|||||
+|[`ADIDA`](models.html#adida)|✅|||||
+|[`CrostonClassic`](models.html#crostonclassic)|✅|||||
+|[`CrostonOptimized`](models.html#crostonoptimized)|✅|||||
+|[`CrostonSBA`](models.html#crostonsba)|✅|||||
+|[`IMAPA`](models.html#imapa)|✅|||||
+|[`TSB`](models.html#tsb)|✅|||||