mkdocs.yml

# mkdocs.yml
site_name: "Time Series with Deep Learning Quick Bite"
site_author: "Lei Ma"
site_description: "Time Series with Deep Learning Quick Bite"
site_url: "https://emptymalei.github.io/deep-learning"
repo_url: "https://github.com/emptymalei/deep-learning"
edit_uri: "blob/main/deep-learning/"
repo_name: "emptymalei/machine-learning"
docs_dir: "dl"


theme:
  name: "material"
  custom_dir: theme/overrides
  # Don't include MkDocs' JavaScript
  include_search_page: false
  search_index_only: true

  # Default values, taken from mkdocs_theme.yml
  language: en
  features:
    # - navigation.instant
    - navigation.sections
    - navigation.tabs
  palette:
    - scheme: default
      primary: black
      accent: deep orange
      toggle:
        icon: material/toggle-switch-off-outline
        name: Switch to dark mode
    - scheme: slate
      primary: red
      accent: red
      toggle:
        icon: material/toggle-switch
        name: Switch to light mode
  font:
    text: Roboto
    code: Roboto Mono
  favicon: assets/logo_badge.svg
  logo: assets/logo_badge.svg



markdown_extensions:
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  - abbr
  - attr_list
  - md_in_html
  - pymdownx.emoji:
      emoji_index: !!python/name:material.extensions.emoji.twemoji
      emoji_generator: !!python/name:material.extensions.emoji.to_svg
  - pymdownx.magiclink
  - pymdownx.snippets:
      check_paths: true
  - pymdownx.superfences:
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          class: mermaid
          format: !!python/name:pymdownx.superfences.fence_code_format
  - pymdownx.tabbed:
      alternate_style: true
  - pymdownx.tasklist
  - pymdownx.arithmatex:
      generic: true
  - toc:
      permalink: "¶"
  - footnotes
  - pymdownx.details


extra_javascript:
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plugins:
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  - git-authors
  - search
  - exclude-search:
      exclude:
        - notebooks/tree_darts
  - tags:
      tags_file: tags.md
  - bibtex:
      bib_dir: "dl/assets/references"
      csl_file: "dl/assets/csl/nature-publishing-group-vancouver.csl" # https://github.com/citation-style-language/styles/blob/master/nature-publishing-group-vancouver.csl
  - mkdocs-jupyter:
      include: ["*.py"]
      ignore: ["notebooks/.ipynb_checkpoints/*"]
      include_source: true
      execute: false
  - print-site:
      add_to_navigation: false
      print_page_title: 'Time Series with Deep Learning'
      add_print_site_banner: false
      # Table of contents
      add_table_of_contents: true
      toc_title: 'Table of Contents'
      toc_depth: 6
      # Content-related
      add_full_urls: false
      enumerate_headings: true
      enumerate_figures: true
      include_css: true
      enabled: true
      exclude:
  # - with-pdf:
  #     author: "Lei Ma"
  #     output_path: pdf/dl-lm.pdf
  #     cover_logo: assets/logo_badge.svg
  #     cover_subtitle: "Deep Learning Quick Bite"
  #     two_columns_level: 3
  #     enabled_if_env: ENABLE_PDF_EXPORT
  #     render_js: true
  #     headless_chrome_path: "/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome"
  #######
  # For Linux
  # - with-pdf:
  #     author: "Lei Ma"
  #     output_path: pdf/dl-lm.pdf
  #     cover_logo: assets/logo_badge.svg
  #     cover_subtitle: "Time Series with Deep Learning Quick Bite"
  #     two_columns_level: 3
  #     enabled_if_env: ENABLE_PDF_EXPORT
  #     render_js: true
  #     headless_chrome_path: "google-chrome"



extra:
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    property: G-9XN0RGHSE1
    feedback:
      title: Was this page helpful?
      ratings:
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          name: This page was helpful
          data: 1
          note: >-
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        - icon: material/emoticon-sad-outline
          name: This page could be improved
          data: 0
          note: >-
            Thanks for your feedback! Help us improve this page by
            using our <a href="https://github.com/emptymalei/deep-learning/issues" target="_blank" rel="noopener">GitHub Issues or Discussions</a>.
  consent:
    title: Cookie consent
    description: >-
      We use cookies to recognize your repeated visits and preferences, as well
      as to measure the effectiveness of our documentation and whether users
      find what they're searching for. With your consent, you're helping us to
      make our documentation better.
  social:
    - icon: fontawesome/brands/github
      link: https://github.com/emptymalei/deep-learning
    - icon: fontawesome/brands/linkedin
      link: https://www.linkedin.com/in/leima137/



nav:
  - "Home": index.md
  - "Engineering Tips":
    - engineering/index.md
    - "Python": engineering/python.md
  - "Fundamentals of Time Series Forecasting":
    - time-series/index.md
    - "Time Series Data":
      - "Terminologies of Time Series Data": time-series/timeseries-data.analysis.md
      - "Box-Cox Transformation": time-series/timeseries-data.box-cox.md
      - "Two-way Fixed Effects": time-series/timeseries-data.analysis.twfe.md
      - "Time Delayed Embedding": time-series/timeseries-data.time-delayed-embedding.md
    - "Data Generating Process":
      - "DGP": time-series/timeseries-datasets.dgp.md
      - "DGP: Langevin Equation": time-series/timeseries-datasets.dgp.langevin.md
    - "Kindergarten Models":
      - "Statistical Models": time-series/timeseries-basics.statistical-models.md
      - "AR": "time-series/timeseries-basics.ar.md"
      - "VAR": time-series/timeseries-basics.var.md
    - "Synthetic Datasets":
      - "Synthetic Time Series": time-series/timeseries-synthetic.md
      - "Creating Synthetic Dataset": time-series/timeseries-datasets.synthetic.md
      - "Augmentation": time-series/timeseries-data.data-augmentation.md
    - "Forecasting":
      - "Time Series Forecasting Tasks": time-series/timeseries-forecast.tasks.md
      - "Naive Forecasts": time-series/timeseries-forecast.naive.md
    - "Evaluation and Metrics":
      - "Time Series Forecasting Evaluation": time-series/timeseries-evaluation.forecasting.md
      - "Time Series Forecasting Metrics": time-series/timeseries-metrics.forecasting.md
      - "CRPS": time-series/timeseries-metrics.forecasting.crps.md
    - "Hierarchical Time Series":
      - "Hierarchical Time Series Data": time-series/timeseries-hierarchical.data.md
      - "Hierarchical Time Series Reconciliation": time-series/timeseries-hierarchical.reconciliation.md
    - "Useful Datasets":
      - time-series/timeseries-datasets.md
      - "Exchange Rate": time-series/timeseries-datasets.ecb-exchange-rate.md
      - "NREL Solar Power Data": time-series/timeseries-datasets.nrel-solar-energy.md
      - "Electricity": time-series/timeseries-datasets.uci-electricity.md
      - "PeMS Traffic Data": time-series/timeseries-datasets.pems.md
  - "Trees":
    - "Tree-based Models": trees/tree.basics.md
    - "Random Forest": trees/tree.random-forest.md
    - "Gradient Boosted Trees": trees/tree.gbdt.md
    - "Forecasting with Trees": trees/tree.darts.md
  - "Fundamentals of Deep Learning":
    - "Deep Learning Introduction": deep-learning-fundamentals/index.md
    - "Learning from Data": deep-learning-fundamentals/learning.md
    - "Neural Networks": deep-learning-fundamentals/neural-net.md
    - "Recurrent Neural Networks": deep-learning-fundamentals/recurrent-neural-networks.md
    - "Convolutional Neural Networks": deep-learning-fundamentals/convolutional-neural-networks.md
    - "Transformers":
      - "Vanilla Transformers": transformers/transformers.vanilla.md
    - "Dynamical Systems":
      - "Why Dynamical Systems": "dynamical-systems/index.md"
      - "Neural ODE": "dynamical-systems/neural-ode.md"
    - "Energy-based Models":
      - "Introduction": energy-based-models/intro.md
      - "Diffusion Models": energy-based-models/ebm.diffusion.md
    - "Generative Models":
      - "Introduction": self-supervised/generative/intro.md
      - "Autoregressive": self-supervised/generative/autoregressive.md
      - "AE": self-supervised/generative/ae.md
      - "VAE": self-supervised/generative/vae.md
      - "Flow": self-supervised/generative/flow.md
      - "GAN": self-supervised/adversarial/gan.md
  - "Time Series Forecasting with Deep Learning":
    - "Introduction": "time-series-deep-learning/index.md"
    - "Pendulum Dataset": "time-series-deep-learning/timeseries.dataset.pendulum.md"
    - "Forecasting with MLP": "time-series-deep-learning/timeseries.feedforward.md"
    - "Forecasting with RNN": time-series-deep-learning/timeseries.rnn.md
    - "Forecasting with Transformers": "time-series-deep-learning/timeseries.transformer.md"
    - "Forecasting with CNN": "time-series-deep-learning/timeseries.cnn.md"
    - "Forecasting with VAE": "time-series-deep-learning/timeseries.vae.md"
    - "Forecasting with Flow": "time-series-deep-learning/timeseries.flow.md"
    - "Forecasting with GAN": "time-series-deep-learning/timeseries.gan.md"
    - "Forecasting with Neural ODE": time-series-deep-learning/timeseries.neural-ode.md
    - "Forecasting with Diffusion Models": time-series-deep-learning/timeseries.deep-learning.timegrad.md
  - "Supplementary":
    - "About Supplementary Materials": supplementary.md
    - "How to Run Our Notebooks": utilities/notebooks-and-utilities.md
    - "Notebooks":
      - "Box-Cox Transformation": notebooks/timeseries_data_box-cox.py
      - "Pendulum Dataset": notebooks/pendulum_dataset.py
      - "Hierarchical Forecasting Using MinT": notebooks/hierarchical_forecasting_mint.py
      - "Tree Basics": notebooks/tree_basics.py
      - "Random Forest Basics": notebooks/tree_random_forest.py
      - "Forecasting with Random Forest using Darts": notebooks/tree_darts_random_forest.py
      - "Forecasting with GBDT using Darts": notebooks/tree_darts_boosted_tree.py
      - "Creating Time Series Dataset (PyTorch)": notebooks/creating_time_series_datasets.py
      - "Forecasting with Feedforward Neural Networks": notebooks/feedforward_neural_netwroks_timeseries.py
      - "Forecasting with RNN": notebooks/rnn_timeseries.py
      - "Forecasting with Transformer": notebooks/transformer_timeseries_univariate.py
      - "Forecasting with NeuralODE": notebooks/neuralode_timeseries.py
      - "Generate Time Series Using Statistics": notebooks/time-series-data-generation.py
      - "Generate Time Series Using VAE": notebooks/time_vae.py
      - "Comparing Time Series": notebooks/timeseries-comparison.py
    - "Small Yet Powerful Concepts":
      - concepts/index.md
      - "Entropy": concepts/entropy.md
      - "Mutual Information": concepts/mutual-information.md
      - "KL Divergence": concepts/kl-divergence.md
      - "f-divergence": concepts/f-divergence.md
      - "ELBO": concepts/elbo.md
      - "Alignment and Uniformity": concepts/alignment-and-uniformity.md
      - "Gini Impurity": concepts/gini-impurity.md
      - "Information Gain": concepts/information-gain.md
      - "Generalization": concepts/generalization.md
      - "DTW": concepts/timeseries-data.dtw.md
      - "DBA": concepts/timeseries-data.dtw-barycenter-averaging.md
    - "Other Deep Learning Topics":
      - "Contrastive":
        - "Introduction": self-supervised/contrastive/intro.md
        - "Deep Infomax": self-supervised/contrastive/deep-infomax.md
        - "Contrastive Predictive Coding": self-supervised/contrastive/contrastive-predictive-coding.md
        - "MADE": self-supervised/generative/made.md
        - "MAF": self-supervised/generative/maf.md
        - "f-GAN": self-supervised/adversarial/f-gan.md
        - "InfoGAN": self-supervised/adversarial/infogan.md
  - "About":
    - "Roadmap": meta/roadmap.md
    - "Changelog": meta/changelog.md