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Fix parsing errors and add mint.json (#243)
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hahnbeelee authored Oct 24, 2023
1 parent 9dce084 commit 2bb5acc
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51 changes: 51 additions & 0 deletions .github/workflows/build-docs.yaml
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name: "build-docs"
on:
push:
branches: ["main"]
pull_request:
branches: ["main"]
workflow_dispatch:

defaults:
run:
shell: bash

jobs:
build-docs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
with:
submodules: 'recursive'
- uses: actions/setup-python@v4
with:
cache: "pip"
python-version: '3.10'
cache-dependency-path: settings.ini
- name: Build docs
run: |
set -ux
python -m pip install --upgrade pip
pip install -Uq nbdev
pip install -e ".[dev]"
mkdir nbs/_extensions
cp -r docs-scripts/mintlify/ nbs/_extensions/
python docs-scripts/update-quarto.py
nbdev_docs
- name: Apply final formats
run: bash ./docs-scripts/docs-final-formatting.bash
- name: Copy over necessary assets
run: |
cp nbs/mint.json _docs/mint.json
cp docs-scripts/imgs/* _docs/
- name: Deploy to Mintlify Docs
if: github.event_name == 'push'
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_branch: docs
publish_dir: ./_docs
# The following lines assign commit authorship to the official GH-Actions bot for deploys to `docs` branch.
# You can swap them out with your own user credentials.
user_name: github-actions[bot]
user_email: 41898282+github-actions[bot]@users.noreply.github.com
4 changes: 4 additions & 0 deletions .gitmodules
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@@ -1,3 +1,7 @@
[submodule "docs/assets"]
path = docs/assets
url = https://github.com/Nixtla/styles.git
[submodule "docs-scripts"]
path = docs-scripts
url = [email protected]:Nixtla/docs.git
branch = scripts
1 change: 1 addition & 0 deletions docs-scripts
Submodule docs-scripts added at d63d02
6 changes: 3 additions & 3 deletions nbs/core.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkOrange\"> Core </span>"
"# Core"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> core.HierarchicalReconciliation </span>"
"# core.HierarchicalReconciliation"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> Example </span>"
"# Example"
]
},
{
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8 changes: 4 additions & 4 deletions nbs/evaluation.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> Accuracy Measurements </span>"
"# Accuracy Measurements"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> Hierarchical Evaluation </span>"
"# Hierarchical Evaluation"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> Example </span>"
"# Example"
]
},
{
Expand Down Expand Up @@ -730,7 +730,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> References </span>\n",
"# References\n",
"\n",
"- [Gneiting, Tilmann, and Adrian E. Raftery. (2007). \n",
"\\\"Strictly proper scoring rules, prediction and estimation\\\". \n",
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4 changes: 2 additions & 2 deletions nbs/examples/Introduction.ipynb
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"Chapter 11: Forecasting hierarchical and grouped series.\". OTexts: Melbourne, Australia. OTexts.com/fpp3 \n",
"Accessed on July 2022.](https://otexts.com/fpp3/hierarchical.html)<br>\n",
"- [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). Data aggregation and information loss. The American \n",
"Economic Review, 58 , 773{787).](http://www.jstor.org/stable/1815532)<br>\n",
"Economic Review, 58 , 773(787).](http://www.jstor.org/stable/1815532)<br>\n",
"- [Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9 , 233–254. \n",
"doi:10.1002/for.3980090304.](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304)<br>\n",
"- [Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). \\\"Optimal forecast reconciliation for\n",
"hierarchical and grouped time series through trace minimization\\\". Journal of the American Statistical Association, \n",
"114 , 804–819. doi:10.1080/01621459.2018.1448825.](https://robjhyndman.com/publications/mint/)<br>\n",
"- [Ben Taieb, S., & Koo, B. (2019). Regularized regression for hierarchical forecasting without \n",
"unbiasedness conditions. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge \n",
"Discovery & Data Mining KDD '19 (p. 1337{1347). New York, NY, USA: Association for Computing Machinery.](https://doi.org/10.1145/3292500.3330976)<br>"
"Discovery & Data Mining KDD '19 (p. 1337(1347). New York, NY, USA: Association for Computing Machinery.](https://doi.org/10.1145/3292500.3330976)<br>"
]
}
],
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2 changes: 1 addition & 1 deletion nbs/examples/TourismSmall.ipynb
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Expand Up @@ -606,7 +606,7 @@
"source": [
"### References\n",
"- [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). Data aggregation and information loss. The American \n",
"Economic Review, 58 , 773{787)](http://www.jstor.org/stable/1815532).\n",
"Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).\n",
"- [Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9 , 233–254. \n",
"doi:10.1002/for.3980090304](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304).<br>\n",
"- [An investigation of aggregate variable time series forecast strategies with specific subaggregate \n",
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25 changes: 10 additions & 15 deletions nbs/methods.ipynb
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Expand Up @@ -13,7 +13,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkOrange\"> Reconciliation Methods </span>"
"# Reconciliation Methods"
]
},
{
Expand Down Expand Up @@ -186,7 +186,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 1. Bottom-Up </span>"
"# 1. Bottom-Up"
]
},
{
Expand All @@ -208,7 +208,7 @@
"\n",
" **References:**<br>\n",
" - [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). \\\"Data aggregation and information loss\\\". The American \n",
" Economic Review, 58 , 773{787)](http://www.jstor.org/stable/1815532).\n",
" Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).\n",
" \"\"\"\n",
" insample = False\n",
"\n",
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 2. Top-Down </span>"
"# 2. Top-Down"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 3. Middle-Out </span>"
"# 3. Middle-Out"
]
},
{
Expand Down Expand Up @@ -990,7 +990,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 4. Min-Trace </span>"
"# 4. Min-Trace"
]
},
{
Expand Down Expand Up @@ -1511,7 +1511,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 5. Optimal Combination </span>"
"# 5. Optimal Combination"
]
},
{
Expand Down Expand Up @@ -1652,7 +1652,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 6. Emp. Risk Minimization </span>"
"# 6. Emp. Risk Minimization"
]
},
{
Expand Down Expand Up @@ -2106,11 +2106,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> References </span>\n",
"# References\n",
"\n",
"### General Reconciliation\n",
"- [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). Data aggregation and information loss. The American \n",
"Economic Review, 58 , 773{787).](http://www.jstor.org/stable/1815532)<br>\n",
"Economic Review, 58 , 773(787).](http://www.jstor.org/stable/1815532)<br>\n",
"- [Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9 , 233–254. \n",
"doi:10.1002/for.3980090304](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304).<br>\n",
"- [An investigation of aggregate variable time series forecast strategies with specific subaggregate \n",
Expand All @@ -2133,11 +2133,6 @@
"- [Puwasala Gamakumara Ph. D. dissertation. Monash University, Econometrics and Business Statistics. \"Probabilistic Forecast Reconciliation\".](https://bridges.monash.edu/articles/thesis/Probabilistic_Forecast_Reconciliation_Theory_and_Applications/11869533)<br>\n",
"- [Taieb, Souhaib Ben and Taylor, James W and Hyndman, Rob J. (2017). Coherent probabilistic forecasts for hierarchical time series. International conference on machine learning ICML.](https://proceedings.mlr.press/v70/taieb17a.html)<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
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79 changes: 79 additions & 0 deletions nbs/mint.json
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{
"$schema": "https://mintlify.com/schema.json",
"name": "Nixtla",
"logo": {
"light": "/light.png",
"dark": "/dark.png"
},
"favicon": "/favicon.svg",
"colors": {
"primary": "#0E0E0E",
"light": "#FAFAFA",
"dark": "#0E0E0E",
"anchors": {
"from": "#2AD0CA",
"to": "#0E00F8"
}
},
"topbarCtaButton": {
"type": "github",
"url": "https://github.com/Nixtla/hierarchicalforecast"
},
"topAnchor": {
"name": "HierarchicalForecast",
"icon": "crown"
},
"navigation": [
{
"group": "",
"pages": ["index.html"]
},
{
"group": "Getting Started",
"pages": [
"examples/installation.html",
"examples/tourismsmall.html",
"examples/introduction.html"
]
},
{
"group": "Tutorials",
"pages": [
{
"group": "Point Reconciliation",
"pages": [
"examples/australiandomestictourism.html",
"examples/australianprisonpopulation.html",
"examples/nonnegativereconciliation.html"
]
},
{
"group": "Probabilistic Reconciliation",
"pages": [
"examples/australiandomestictourism-intervals.html",
"examples/australiandomestictourism-bootstraped-intervals.html",
"examples/australiandomestictourism-permbu-intervals.html",
"examples/tourismlarge-evaluation.html"
]
},
{
"group": "ML Forecast Reconciliation",
"pages": [
"examples/hierarchicalforecast-gluonts.html",
"examples/mlframeworksexample.html"
]
}
]
},
{
"group": "API Reference",
"pages": [
"core.html",
"methods.html",
"probabilistic_methods.html",
"evaluation.html",
"utils.html"
]
}
]
}
10 changes: 5 additions & 5 deletions nbs/probabilistic_methods.ipynb
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Expand Up @@ -14,7 +14,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkOrange\"> Probabilistic Methods </span>"
"# Probabilistic Methods"
]
},
{
Expand Down Expand Up @@ -63,7 +63,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 1. Normality </span>"
"# 1. Normality"
]
},
{
Expand Down Expand Up @@ -192,7 +192,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 2. Bootstrap </span>"
"# 2. Bootstrap"
]
},
{
Expand Down Expand Up @@ -324,7 +324,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> 3. PERMBU </span>"
"# 3. PERMBU"
]
},
{
Expand Down Expand Up @@ -727,7 +727,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:DarkBlue\"> References </span>\n",
"# References\n",
"\n",
"- [Rob J. Hyndman and George Athanasopoulos (2018). \n",
"\"Forecasting principles and practice, Reconciled distributional forecasts\".](https://otexts.com/fpp3/rec-prob.html)<br>\n",
Expand Down
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