v1.0

.all-contributorsrc

+{
+  "files": [
+    "README.md"
+  ],
+  "imageSize": 100,
+  "commit": false,
+  "contributors": [
+    {
+      "login": "FedericoGarza",
+      "name": "fede",
+      "avatar_url": "https://avatars.githubusercontent.com/u/10517170?v=4",
+      "profile": "https://github.com/FedericoGarza",
+      "contributions": [
+        "code",
+        "maintenance"
+      ]
+    },
+    {
+      "login": "cchallu",
+      "name": "Cristian Challu",
+      "avatar_url": "https://avatars.githubusercontent.com/u/31133398?v=4",
+      "profile": "https://github.com/cchallu",
+      "contributions": [
+        "code",
+        "maintenance"
+      ]
+    },
+    {
+      "login": "jmoralez",
+      "name": "José Morales",
+      "avatar_url": "https://avatars.githubusercontent.com/u/8473587?v=4",
+      "profile": "https://github.com/jmoralez",
+      "contributions": [
+        "code",
+        "maintenance"
+      ]
+    },
+    {
+      "login": "mergenthaler",
+      "name": "mergenthaler",
+      "avatar_url": "https://avatars.githubusercontent.com/u/4086186?v=4",
+      "profile": "https://github.com/mergenthaler",
+      "contributions": [
+        "doc",
+        "code"
+      ]
+    },
+    {
+      "login": "kdgutier",
+      "name": "Kin",
+      "avatar_url": "https://avatars.githubusercontent.com/u/19935241?v=4",
+      "profile": "https://github.com/kdgutier",
+      "contributions": [
+        "code",
+        "bug",
+        "data"
+      ]
+    },
+    {
+      "login": "gdevos010",
+      "name": "Greg DeVos",
+      "avatar_url": "https://avatars.githubusercontent.com/u/15316026?v=4",
+      "profile": "https://github.com/gdevos010",
+      "contributions": [
+        "ideas"
+      ]
+    },
+    {
+      "login": "alejandroxag",
+      "name": "Alejandro",
+      "avatar_url": "https://avatars.githubusercontent.com/u/64334543?v=4",
+      "profile": "https://github.com/alejandroxag",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "stefanialvs",
+      "name": "stefanialvs",
+      "avatar_url": "https://avatars.githubusercontent.com/u/48966177?v=4",
+      "profile": "http://lavattiata.com",
+      "contributions": [
+        "design"
+      ]
+    },
+    {
+      "login": "eltociear",
+      "name": "Ikko Ashimine",
+      "avatar_url": "https://avatars.githubusercontent.com/u/22633385?v=4",
+      "profile": "https://bandism.net/",
+      "contributions": [
+        "bug"
+      ]
+    },
+    {
+      "login": "vglaucus",
+      "name": "vglaucus",
+      "avatar_url": "https://avatars.githubusercontent.com/u/75549033?v=4",
+      "profile": "https://github.com/vglaucus",
+      "contributions": [
+        "bug"
+      ]
+    },
+    {
+      "login": "pitmonticone",
+      "name": "Pietro Monticone",
+      "avatar_url": "https://avatars.githubusercontent.com/u/38562595?v=4",
+      "profile": "https://github.com/pitmonticone",
+      "contributions": [
+        "bug"
+      ]
+    }
+  ],
+  "contributorsPerLine": 7,
+  "projectName": "neuralforecast",
+  "projectOwner": "Nixtla",
+  "repoType": "github",
+  "repoHost": "https://github.com",
+  "skipCi": true
+}

.circleci/config.yml

+version: 2.1
+jobs:
+  nbdev-tests:
+    resource_class: xlarge
+    docker:
+      - image: mambaorg/micromamba:1.5-focal
+    steps:
+      - checkout
+      - run:
+          name: Install dependencies
+          command: micromamba install -n base -c conda-forge -y python=3.10 git -f environment-cpu.yml
+      - run:
+          name: Run nbdev tests
+          command: |
+            eval "$(micromamba shell hook --shell bash)"
+            micromamba activate base
+            pip install ".[dev]"
+            nbdev_test --do_print --timing --n_workers 1
+  test-model-performance:
+    resource_class: xlarge
+    docker:
+      - image: mambaorg/micromamba:1.5-focal
+    steps:
+      - checkout
+      - run:
+          name: Install dependencies
+          command: micromamba install -n base -c conda-forge -y python=3.10 -f environment-cpu.yml
+      - run:
+          name: Run model performance tests
+          command: |
+            eval "$(micromamba shell hook --shell bash)"
+            micromamba activate base
+            pip install -e ".[dev]"
+            export LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH
+            cd ./action_files/test_models/
+            pip install -r requirements.txt
+            python -m src.models
+            python -m src.evaluation
+            cd ../../
+      - store_artifacts:
+          path: ./action_files/test_models/data/evaluation.csv
+          destination: evaluation.csv
+  test-model-performance2:
+    resource_class: xlarge
+    docker:
+      - image: mambaorg/micromamba:1.5-focal
+    steps:
+      - checkout
+      - run:
+          name: Install dependencies
+          command: micromamba install -n base -c conda-forge -y python=3.10 -f environment-cpu.yml
+      - run:
+          name: Run model performance tests
+          command: |
+            eval "$(micromamba shell hook --shell bash)"
+            micromamba activate base
+            pip install -e ".[dev]"
+            export LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH
+            cd ./action_files/test_models/
+            pip install -r requirements.txt
+            python -m src.models2
+            python -m src.evaluation2
+            cd ../../
+      - store_artifacts:
+          path: ./action_files/test_models/data/evaluation.csv
+          destination: evaluation.csv
+
+workflows:
+  sample:
+    jobs:
+      - nbdev-tests
+      - test-model-performance
+      - test-model-performance2

.gitignore

+.ipynb_checkpoints
+__pycache__
+*.egg-info
+Gemfile*
+Gemfile.lock
+docs/_site
+build
+dist
+.vscode
+*.gif
+*.csv
+*/data/*
+*.parquet
+tmp
+_docs/
+sidebar.yml
+.DS_Store
+.gitattributes
+.gitconfig
+nbs/.last_checked
+_proc
+lightning_logs/
+/**/data/
+debug_run
+
+longhorizon/
+_old/

.pre-commit-config.yaml

+fail_fast: true
+
+repos:
+  - repo: local
+    hooks:
+      - id: imports_with_code
+        name: Cells with imports and code
+        entry: python action_files/imports_with_code.py
+        language: system
+  - repo: https://github.com/fastai/nbdev
+    rev: 2.2.10
+    hooks:
+      - id: nbdev_clean
+      - id: nbdev_export
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.2.1
+    hooks:
+      - id: ruff
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.8.0
+    hooks:
+      - id: mypy
+        args: [--ignore-missing-imports]
+        exclude: 'setup.py'

CODE_OF_CONDUCT.md

+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+ops@nixtla.io.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

CONTRIBUTING.md

+# How to contribute
+
+## Did you find a bug?
+
+* Ensure the bug was not already reported by searching on GitHub under Issues.
+* If you're unable to find an open issue addressing the problem, open a new one. Be sure to include a title and clear description, as much relevant information as possible, and a code sample or an executable test case demonstrating the expected behavior that is not occurring.
+* Be sure to add the complete error messages.
+
+## Do you have a feature request?
+
+* Ensure that it hasn't been yet implemented in the `main` branch of the repository and that there's not an Issue requesting it yet.
+* Open a new issue and make sure to describe it clearly, mention how it improves the project and why its useful.
+
+## Do you want to fix a bug or implement a feature?
+
+Bug fixes and features are added through pull requests (PRs).
+
+##  PR submission guidelines
+
+* Keep each PR focused. While it's more convenient, do not combine several unrelated fixes together. Create as many branches as needing to keep each PR focused.
+* Ensure that your PR includes a test that fails without your patch, and passes with it.
+* Ensure the PR description clearly describes the problem and solution. Include the relevant issue number if applicable.
+* Do not mix style changes/fixes with "functional" changes. It's very difficult to review such PRs and it most likely get rejected.
+* Do not add/remove vertical whitespace. Preserve the original style of the file you edit as much as you can.
+* Do not turn an already submitted PR into your development playground. If after you submitted PR, you discovered that more work is needed - close the PR, do the required work and then submit a new PR. Otherwise each of your commits requires attention from maintainers of the project.
+* If, however, you submitted a PR and received a request for changes, you should proceed with commits inside that PR, so that the maintainer can see the incremental fixes and won't need to review the whole PR again. In the exception case where you realize it'll take many many commits to complete the requests, then it's probably best to close the PR, do the work and then submit it again. Use common sense where you'd choose one way over another.
+
+### Local setup for working on a PR
+
+#### Clone the repository
+* HTTPS: `git clone https://github.com/Nixtla/neuralforecast.git`
+* SSH: `git clone git@github.com:Nixtla/neuralforecast.git`
+* GitHub CLI: `gh repo clone Nixtla/neuralforecast`
+
+#### Set up a conda environment
+The repo comes with an `environment.yml` file which contains the libraries needed to run all the tests. In order to set up the environment you must have `conda` installed, we recommend [miniconda](https://docs.conda.io/en/latest/miniconda.html).
+
+Once you have `conda` go to the top level directory of the repository and run the following lines:
+```
+conda create -n neuralforecast python=3.10
+conda activate neuralforecast
+```
+Then, run one of the following commands:
+```
+conda env update -f environment-cpu.yml  # choose this if you want to install the CPU-only version of neuralforecast
+conda env update -f environment-cuda.yml # choose this if you want to install the CUDA-enabled version of neuralforecast
+```
+
+#### Install the library
+Once you have your environment setup, activate it using `conda activate neuralforecast` and then install the library in editable mode using `pip install -e ".[dev]"`
+
+#### Install git hooks
+Before doing any changes to the code, please install the git hooks that run automatic scripts during each commit and merge to strip the notebooks of superfluous metadata (and avoid merge conflicts).
+```
+nbdev_install_hooks
+pre-commit install
+```
+
+### Preview Changes
+You can preview changes in your local browser before pushing by using the `nbdev_preview`.
+
+### Building the library
+The library is built using the notebooks contained in the `nbs` folder. If you want to make any changes to the library you have to find the relevant notebook, make your changes and then call:
+```
+nbdev_export
+```
+
+### Running tests
+If you're working on the local interface you can just use `nbdev_test --n_workers 1 --do_print --timing`. 
+
+### Cleaning notebooks
+Since the notebooks output cells can vary from run to run (even if they produce the same outputs) the notebooks are cleaned before committing them. Please make sure to run `nbdev_clean --clear_all` before committing your changes. If you clean the library's notebooks with this command please backtrack the changes you make to the example notebooks `git checkout nbs/examples`, unless you intend to change the examples.
+
+## Do you want to contribute to the documentation?
+
+* Docs are automatically created from the notebooks in the `nbs` folder.
+* In order to modify the documentation:
+    1. Find the relevant notebook.
+    2. Make your changes.
+    3. Run all cells.
+    4. If you are modifying library notebooks (not in `nbs/examples`), clean all outputs using `Edit > Clear All Outputs`.
+    5. Run `nbdev_preview`.
+    6. Clean the notebook metadata using `nbdev_clean`.

LICENSE

+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
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+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
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+      "You" (or "Your") shall mean an individual or Legal Entity
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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
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+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

MANIFEST.in

+## Include List
+
+include README.md
+include LICENSE
+include settings.ini
+
+recursive-include neuralforecast *
+
+
+## Exclude List
+
+exclude CONTRIBUTING.md
+exclude Makefile
+exclude environment.yml
+
+exclude .gitconfig
+exclude .gitignore
+exclude .gitmodules
+
+recursive-exclude .github *
+recursive-exclude docs *
+recursive-exclude examples *
+recursive-exclude experiments *
+recursive-exclude nbs *

README.md

+# iTransformer
+iTransformer可以跨多个时间步查看一个特征，能高效利用长程时序特征，本步骤适于时序预测库neuralforecast中的iTransformer算法，其它算法的使用依此类推。
+## 论文
+`iTransformer: Inverted Transformers Are Effective for Time Series Forecasting`
+- https://arxiv.org/pdf/2310.06625
+
+## 模型结构
+采用标准的transformer decoder结构，对于backbone，无需修改transformer标准结构的代码即可实现本算法。
+<div align=center>
+    <img src="./doc/transformer.png"/>
+</div>
+
+## 算法原理
+iTransformer通过简单地转置输入的形状来实现跨多个时间步查看一个特征，模型不是对输入的子序列进行令牌化，而是对整个输入序列进行令牌化，通过这种方式，注意力层可以专注于学习多元相关性，而前馈网络则负责对整个输入序列进行编码。
+<div align=center>
+    <img src="./doc/iTransformer.png"/>
+</div>
+
+## 环境配置
+```
+mv neuralforecast-itransformer_pytorch neuralforecast # 去框架名后缀
+```
+
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10-py38
+# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：ffa1f63239fc
+docker run -it --shm-size=32G -v $PWD/neuralforecast:/home/neuralforecast -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name neuralforecast <your IMAGE ID> bash
+cd /home/neuralforecast
+pip install -r requirements.txt # requirements.txt
+```
+### Dockerfile（方法二）
+```
+cd neuralforecast/docker
+docker build --no-cache -t neuralforecast:latest .
+docker run --shm-size=32G --name neuralforecast -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../neuralforecast:/home/neuralforecast -it neuralforecast bash
+# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
+```
+### Anaconda（方法三）
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
+- https://developer.hpccube.com/tool/
+```
+DTK驱动:dtk23.10
+python:python3.8
+torch:2.1.0
+torchvision:0.16.0
+```
+
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
+
+2、其它非特殊库参照requirements.txt安装
+```
+pip install -r requirements.txt # requirements.txt
+```
+
+## 数据集
+本步骤说明采用LongHorizon中的`ETTm2`
+- https://nhits-experiments.s3.amazonaws.com/datasets.zip
+
+数据目录结构如下：
+```
+ETT-small/longhorizon/datasets/ETTm2
+    ├── df_x.csv
+    ├── df_y.csv
+    ├── M
+    ├── ├── df_x.csv
+    ├── └── df_y.csv
+    └── S
+    ├── ├── df_x.csv
+    ├── └── df_y.csv
+```
+
+## 训练
+### 单机单卡
+```
+export HIP_VISIBLE_DEVICES=0
+cd neuralforecast
+python trainval.py # 该库中的iTransformer目前仅支持单指标预测的训练与推理，多指标预测敬请等待作者开源。
+```
+更多资料可参考源项目的[`README_origin`](./README_origin.md)
+
+## 推理
+```
+export HIP_VISIBLE_DEVICES=0
+python infer.py
+# 默认按天预测, 故pred_len为96。
+```
+
+## result
+# 输入
+```
+ds,unique_id,y
+2016-07-01 00:00:00,OT,1.0180321560148238
+2016-07-01 00:15:00,OT,0.9801244795019736
+2016-07-01 00:30:00,OT,0.9042228236873991
+2016-07-01 00:45:00,OT,0.8852256693365586
+                   ...
+2018-02-20 23:00:00,OT,-1.5813253301696957
+2018-02-20 23:15:00,OT,-1.5813253301696957
+2018-02-20 23:30:00,OT,-1.5623282581688442
+2018-02-20 23:45:00,OT,-1.5623282581688442
+```
+# 输出
+```
+      unique_id     ds               iTransformer
+0         OT 2018-02-21 00:00:00     -1.567348
+1         OT 2018-02-21 00:15:00     -1.577499
+2         OT 2018-02-21 00:30:00     -1.579781
+3         OT 2018-02-21 00:45:00     -1.587281
+4         OT 2018-02-21 01:00:00     -1.578477
+                  ...
+92        OT 2018-02-21 23:00:00     -1.569653
+93        OT 2018-02-21 23:15:00     -1.546048
+94        OT 2018-02-21 23:30:00     -1.561576
+95        OT 2018-02-21 23:45:00     -1.542416
+```
+
+### 精度
+测试数据：[`ETTm2`](./ETT-small/longhorizon/datasets/ETTm2/df_y.csv)中划出一部分作验证集，max_steps=1000，推理框架：pytorch。
+
+|  device   | valid_loss |
+|:---------:|:----------:|
+| DCU Z100L |   0.226   |
+| GPU V100S |   0.224   |
+
+## 应用场景
+### 算法类别
+`时序预测`
+### 热点应用行业
+`金融,运维,电商,制造,能源,医疗`
+## 源码仓库及问题反馈
+- http://developer.hpccube.com/codes/modelzoo/neuralforecast-itransformer_pytorch.git
+## 参考资料
+- https://github.com/thuml/iTransformer.git

README_origin.md

+# Nixtla   [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/neuralforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting)  [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ)
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_new.png">
+<h1 align="center">Neural 🧠 Forecast</h1>
+<h3 align="center">User friendly state-of-the-art neural forecasting models</h3>
+
+[![CI](https://github.com/Nixtla/neuralforecast/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/Nixtla/neuralforecast/actions/workflows/ci.yaml)
+[![Python](https://img.shields.io/pypi/pyversions/neuralforecast)](https://pypi.org/project/neuralforecast/)
+[![PyPi](https://img.shields.io/pypi/v/neuralforecast?color=blue)](https://pypi.org/project/neuralforecast/)
+[![conda-nixtla](https://img.shields.io/conda/vn/conda-forge/neuralforecast?color=seagreen&label=conda)](https://anaconda.org/conda-forge/neuralforecast)
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Nixtla/neuralforecast/blob/main/LICENSE)
+[![docs](https://img.shields.io/website-up-down-green-red/http/nixtla.github.io/neuralforecast.svg?label=docs)](https://nixtla.github.io/neuralforecast/)  
+<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
+[![All Contributors](https://img.shields.io/badge/all_contributors-11-orange.svg?style=flat-square)](#contributors-)
+<!-- ALL-CONTRIBUTORS-BADGE:END -->
+
+**NeuralForecast** offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like `RNN` to the latest transformers: `MLP`, `LSTM`, `GRU`,`RNN`,`TCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `DLinear`, `NLinear`,`TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`,`StemGNN`, and `TimesNet`.
+</div>
+
+## Installation
+
+You can install `NeuralForecast` with:
+
+```python
+pip install neuralforecast
+```
+
+or 
+
+```python
+conda install -c conda-forge neuralforecast
+``` 
+Vist our [Installation Guide](https://nixtla.github.io/neuralforecast/examples/installation.html) for further details.
+
+## Quick Start
+
+**Minimal Example**
+
+```python
+from neuralforecast import NeuralForecast
+from neuralforecast.models import NBEATS
+from neuralforecast.utils import AirPassengersDF
+
+nf = NeuralForecast(
+    models = [NBEATS(input_size=24, h=12, max_steps=100)],
+    freq = 'M'
+)
+
+nf.fit(df=AirPassengersDF)
+nf.predict()
+```
+
+**Get Started with this [quick guide](https://nixtla.github.io/neuralforecast/examples/getting_started.html).**
+
+## Why? 
+
+There is a shared belief in Neural forecasting methods' capacity to improve forecasting pipeline's accuracy and efficiency.
+
+Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created `NeuralForecast`, a library favoring proven accurate and efficient models focusing on their usability.
+
+## Features 
+
+* Fast and accurate implementations of `MLP`, `LSTM`, `GRU`,`RNN`,`TCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `DLinear`,`TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`,`StemGNN`, and `TimesNet`. See the entire [collection here](https://nixtla.github.io/neuralforecast/models.html).
+* Support for exogenous variables and static covariates.
+* Interpretability methods for trend, seasonality and exogenous components.
+* Probabilistic Forecasting with adapters for quantile losses and parametric distributions.
+* Train and Evaluation Losses with scale-dependent, percentage and scale independent errors, and parametric likelihoods.
+* Automatic Model Selection with distributed automatic hyperparameter tuning.
+* Familiar sklearn syntax: `.fit` and `.predict`.
+
+## Highlights
+
+* Official `NHITS` implementation, published at AAAI 2023. See [paper](https://ojs.aaai.org/index.php/AAAI/article/view/25854) and [experiments](./experiments/).
+* Official `NBEATSx` implementation, published at the International Journal of Forecasting. See [paper](https://www.sciencedirect.com/science/article/pii/S0169207022000413).
+* Unified with`StatsForecast`, `MLForecast`, and `HierarchicalForecast` interface `NeuralForecast().fit(Y_df).predict()`, inputs and outputs.
+* Built-in integrations with `utilsforecast` and `coreforecast` for visualization and data-wrangling efficient methods.
+* Integrations with `Ray` and `Optuna` for automatic hyperparameter optimization.
+* Predict with little to no history using Transfer learning. Check the experiments [here](https://github.com/Nixtla/transfer-learning-time-series).
+
+Missing something? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)
+
+## Examples and Guides
+
+The [documentation page](https://nixtla.github.io/neuralforecast/) contains all the examples and tutorials.
+
+📈 [Automatic Hyperparameter Optimization](https://nixtla.github.io/neuralforecast/examples/automatic_hyperparameter_tuning.html): Easy and Scalable Automatic Hyperparameter Optimization with `Auto` models on `Ray` or `Optuna`.
+
+🌡️ [Exogenous Regressors](https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html): How to incorporate static or temporal exogenous covariates like weather or prices.
+
+🔌 [Transformer Models](https://nixtla.github.io/neuralforecast/examples/longhorizon_with_transformers.html): Learn how to forecast with many state-of-the-art Transformers models.
+
+👑 [Hierarchical Forecasting](https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html): forecast series with very few non-zero observations. 
+
+👩‍🔬 [Add Your Own Model](https://nixtla.github.io/neuralforecast/examples/how_to_add_models.html): Learn how to add a new model to the library.
+
+## Models
+
+|Model | Structure | Sampling | Point Forecast | Probabilistic Forecast | Exogenous features | `Auto` Model
+|:------|:-------------:|:-----------------:|:---------------------:|:----------------------------:|:---------------------:|:---------------------:|
+|[LSTM](https://nixtlaverse.nixtla.io/neuralforecast/models.lstm.html)| RNN | recurrent |✅|✅|✅|✅|
+|[GRU](https://nixtlaverse.nixtla.io/neuralforecast/models.gru.html)| RNN | recurrent |✅|✅|✅|✅|
+|[RNN](https://nixtlaverse.nixtla.io/neuralforecast/models.rnn.html)| RNN | recurrent |✅|✅|✅|✅|
+|[DilatedRNN](https://nixtlaverse.nixtla.io/neuralforecast/models.dilated_rnn.html)| RNN | recurrent |✅|✅|✅|✅|
+|[DeepAR](https://nixtlaverse.nixtla.io/neuralforecast/models.deepar.html)| RNN | recurrent ||✅|✅|✅|
+|[TCN](https://nixtlaverse.nixtla.io/neuralforecast/models.tcn.html)| CNN | recurrent |✅|✅|✅|✅|
+|[TimesNet](https://nixtlaverse.nixtla.io/neuralforecast/models.timesnet.html)| CNN | windows |✅|✅||✅|
+|[DLinear](https://nixtlaverse.nixtla.io/neuralforecast/models.dlinear.html)| Linear | windows |✅|✅||✅|
+|[MLP](https://nixtlaverse.nixtla.io/neuralforecast/models.mlp.html)| MLP | windows |✅|✅|✅|✅|
+|[NBEATS](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeats.html)| MLP | windows |✅|✅||✅|
+|[NBEATSx](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeatsx.html)| MLP | windows |✅|✅|✅|✅|
+|[NHITS](https://nixtlaverse.nixtla.io/neuralforecast/models.nhits.html)| MLP | windows |✅|✅|✅|✅|
+|[TFT](https://nixtlaverse.nixtla.io/neuralforecast/models.tft.html)| Transformer | windows |✅|✅|✅|✅|
+|[Transformer](https://nixtlaverse.nixtla.io/neuralforecast/models.vanillatransformer.html)| Transformer | windows |✅|✅|✅|✅|
+|[Informer](https://nixtlaverse.nixtla.io/neuralforecast/models.informer.html)| Transformer | windows |✅|✅|✅|✅|
+|[Autoformer](https://nixtlaverse.nixtla.io/neuralforecast/models.autoformer.html)| Transformer | windows |✅|✅|✅|✅|
+|[FEDFormer](https://nixtlaverse.nixtla.io/neuralforecast/models.fedformer.html)| Transformer | windows |✅|✅|✅|✅|
+|[PatchTST](https://nixtlaverse.nixtla.io/neuralforecast/models.patchtst.html)| Transformer | windows |✅|✅||✅|
+|[StemGNN](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html)| GNN | multivariate |✅|||✅|
+
+Missing a model? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)
+
+## How to contribute
+If you wish to contribute to the project, please refer to our [contribution guidelines](https://github.com/Nixtla/neuralforecast/blob/main/CONTRIBUTING.md).
+
+## References
+This work is highly influenced by the fantastic work of previous contributors and other scholars on the neural forecasting methods presented here. We want to highlight the work of [Boris Oreshkin](https://arxiv.org/abs/1905.10437), [Slawek Smyl](https://www.sciencedirect.com/science/article/pii/S0169207019301153), [Bryan Lim](https://www.sciencedirect.com/science/article/pii/S0169207021000637), and [David Salinas](https://arxiv.org/abs/1704.04110). We refer to [Benidis et al.](https://arxiv.org/abs/2004.10240) for a comprehensive survey of neural forecasting methods.
+
+## Contributors ✨
+
+Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
+<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
+<!-- prettier-ignore-start -->
+<!-- markdownlint-disable -->
+<table>
+  <tbody>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/FedericoGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="fede"/><br /><sub><b>fede</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=FedericoGarza" title="Code">💻</a> <a href="#maintenance-FedericoGarza" title="Maintenance">🚧</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/cchallu"><img src="https://avatars.githubusercontent.com/u/31133398?v=4?s=100" width="100px;" alt="Cristian Challu"/><br /><sub><b>Cristian Challu</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=cchallu" title="Code">💻</a> <a href="#maintenance-cchallu" title="Maintenance">🚧</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/jmoralez"><img src="https://avatars.githubusercontent.com/u/8473587?v=4?s=100" width="100px;" alt="José Morales"/><br /><sub><b>José Morales</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=jmoralez" title="Code">💻</a> <a href="#maintenance-jmoralez" title="Maintenance">🚧</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/mergenthaler"><img src="https://avatars.githubusercontent.com/u/4086186?v=4?s=100" width="100px;" alt="mergenthaler"/><br /><sub><b>mergenthaler</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=mergenthaler" title="Documentation">📖</a> <a href="https://github.com/Nixtla/neuralforecast/commits?author=mergenthaler" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/kdgutier"><img src="https://avatars.githubusercontent.com/u/19935241?v=4?s=100" width="100px;" alt="Kin"/><br /><sub><b>Kin</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=kdgutier" title="Code">💻</a> <a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Akdgutier" title="Bug reports">🐛</a> <a href="#data-kdgutier" title="Data">🔣</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/gdevos010"><img src="https://avatars.githubusercontent.com/u/15316026?v=4?s=100" width="100px;" alt="Greg DeVos"/><br /><sub><b>Greg DeVos</b></sub></a><br /><a href="#ideas-gdevos010" title="Ideas, Planning, & Feedback">🤔</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/alejandroxag"><img src="https://avatars.githubusercontent.com/u/64334543?v=4?s=100" width="100px;" alt="Alejandro"/><br /><sub><b>Alejandro</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=alejandroxag" title="Code">💻</a></td>
+    </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="http://lavattiata.com"><img src="https://avatars.githubusercontent.com/u/48966177?v=4?s=100" width="100px;" alt="stefanialvs"/><br /><sub><b>stefanialvs</b></sub></a><br /><a href="#design-stefanialvs" title="Design">🎨</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://bandism.net/"><img src="https://avatars.githubusercontent.com/u/22633385?v=4?s=100" width="100px;" alt="Ikko Ashimine"/><br /><sub><b>Ikko Ashimine</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Aeltociear" title="Bug reports">🐛</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/vglaucus"><img src="https://avatars.githubusercontent.com/u/75549033?v=4?s=100" width="100px;" alt="vglaucus"/><br /><sub><b>vglaucus</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Avglaucus" title="Bug reports">🐛</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/pitmonticone"><img src="https://avatars.githubusercontent.com/u/38562595?v=4?s=100" width="100px;" alt="Pietro Monticone"/><br /><sub><b>Pietro Monticone</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Apitmonticone" title="Bug reports">🐛</a></td>
+    </tr>
+  </tbody>
+</table>
+
+<!-- markdownlint-restore -->
+<!-- prettier-ignore-end -->
+
+<!-- ALL-CONTRIBUTORS-LIST:END -->
+
+This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!

action_files/imports_with_code.py

+import sys
+import warnings
+from pathlib import Path
+
+from nbdev.processors import NBProcessor, _do_eval
+
+
+def check_nb(nb_path: str) -> None:
+    with warnings.catch_warnings(record=True) as issued_warnings:
+        NBProcessor(nb_path, _do_eval, process=True)
+    if any(
+        "Found cells containing imports and other code" in str(w)
+        for w in issued_warnings
+    ):
+        print(f"{nb_path} has cells containing imports and code.")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    repo_root = Path(__file__).parents[1]
+    for nb_path in (repo_root / "nbs").glob("*.ipynb"):
+        check_nb(str(nb_path))

action_files/test_models/requirements.txt

+fire
+datasetsforecast
\ No newline at end of file

action_files/test_models/src/data.py

+import fire
+import pandas as pd
+from datasetsforecast.m3 import M3, M3Info
+
+
+dict_datasets = {
+    'M3': (M3, M3Info),
+}
+
+def get_data(directory: str, dataset: str, group: str, train: bool = True):
+    if dataset not in dict_datasets.keys():
+        raise Exception(f'dataset {dataset} not found')
+
+    dataclass, datainfo = dict_datasets[dataset]
+    if group not in datainfo.groups:
+        raise Exception(f'group {group} not found for {dataset}')
+
+    Y_df, *_ = dataclass.load(directory, group)
+
+    horizon = datainfo[group].horizon
+    freq = datainfo[group].freq
+    seasonality = datainfo[group].seasonality
+    Y_df_test = Y_df.groupby('unique_id').tail(horizon)
+    Y_df = Y_df.drop(Y_df_test.index)
+
+    if train:
+        return Y_df, horizon, freq, seasonality
+
+    return Y_df_test, horizon, freq, seasonality
+
+def save_data(dataset: str, group: str, train: bool = True):
+    df, *_ = get_data('data', dataset, group, train)
+    if train:
+        df.to_csv(f'data/{dataset}-{group}.csv', index=False)
+    else:
+        df.to_csv(f'data/{dataset}-{group}-test.csv', index=False)
+
+
+if __name__=="__main__":
+    fire.Fire(save_data)
\ No newline at end of file

action_files/test_models/src/evaluation.py

+from itertools import product
+
+import numpy as np
+import pandas as pd
+
+from src.data import get_data
+
+def mae(y, y_hat, axis):
+    delta_y = np.abs(y - y_hat)
+    mae = np.average(delta_y, axis=axis)
+    return mae
+
+def smape(y, y_hat, axis):
+    delta_y = np.abs(y - y_hat)
+    scale = np.abs(y) + np.abs(y_hat)
+    smape = delta_y / scale
+    smape = 200 * np.average(smape, axis=axis)
+    return smape
+
+def evaluate(model: str, dataset: str, group: str):
+    try:
+        forecast = pd.read_csv(f'data/{model}-forecasts-{dataset}-{group}.csv')
+    except:
+        return None
+    y_test, horizon, freq, seasonality = get_data('data/', dataset, group, False)
+    y_hat = forecast[model].values.reshape(-1, horizon)
+    y_test = y_test['y'].values.reshape(-1, horizon)
+
+    evals = {}
+    for metric in (mae, smape):
+        metric_name = metric.__name__
+        loss = metric(y_test, y_hat, axis=1).mean()
+        evals[metric_name] = loss 
+
+    evals = pd.DataFrame(evals, index=[f'{dataset}_{group}']).rename_axis('dataset').reset_index()
+    times = pd.read_csv(f'data/{model}-time-{dataset}-{group}.csv')
+    evals = pd.concat([evals, times], axis=1)
+
+    return evals
+
+
+if __name__ == '__main__':
+    groups = ['Monthly']
+    models = ['AutoDilatedRNN', 'RNN', 'TCN', 'DeepAR',
+              'NHITS', 'TFT', 'AutoMLP', 'DLinear', 'VanillaTransformer']
+    datasets = ['M3']
+    evaluation = [evaluate(model, dataset, group) for model, group in product(models, groups) for dataset in datasets]
+    evaluation = [eval_ for eval_ in evaluation if eval_ is not None]
+    evaluation = pd.concat(evaluation)
+    evaluation = evaluation[['dataset', 'model', 'time', 'mae', 'smape']]
+    evaluation['time'] /= 60 #minutes
+    evaluation = evaluation.set_index(['dataset', 'model']).stack().reset_index()
+    evaluation.columns = ['dataset', 'model', 'metric', 'val']
+    evaluation = evaluation.set_index(['dataset', 'metric', 'model']).unstack().round(3)
+    evaluation = evaluation.droplevel(0, 1).reset_index()
+    evaluation['AutoARIMA'] = [666.82, 15.35, 3.000]
+    evaluation.to_csv('data/evaluation.csv')
+    print(evaluation.T)

action_files/test_models/src/evaluation2.py

+from itertools import product
+
+import numpy as np
+import pandas as pd
+
+from src.data import get_data
+
+def mae(y, y_hat, axis):
+    delta_y = np.abs(y - y_hat)
+    mae = np.average(delta_y, axis=axis)
+    return mae
+
+def smape(y, y_hat, axis):
+    delta_y = np.abs(y - y_hat)
+    scale = np.abs(y) + np.abs(y_hat)
+    smape = delta_y / scale
+    smape = 200 * np.average(smape, axis=axis)
+    return smape
+
+def evaluate(model: str, dataset: str, group: str):
+    try:
+        forecast = pd.read_csv(f'data/{model}-forecasts-{dataset}-{group}.csv')
+    except:
+        return None
+    y_test, horizon, freq, seasonality = get_data('data/', dataset, group, False)
+    y_hat = forecast[model].values.reshape(-1, horizon)
+    y_test = y_test['y'].values.reshape(-1, horizon)
+
+    evals = {}
+    for metric in (mae, smape):
+        metric_name = metric.__name__
+        loss = metric(y_test, y_hat, axis=1).mean()
+        evals[metric_name] = loss 
+
+    evals = pd.DataFrame(evals, index=[f'{dataset}_{group}']).rename_axis('dataset').reset_index()
+    times = pd.read_csv(f'data/{model}-time-{dataset}-{group}.csv')
+    evals = pd.concat([evals, times], axis=1)
+
+    return evals
+
+
+if __name__ == '__main__':
+    groups = ['Monthly']
+    models = ['LSTM', 'DilatedRNN', 'GRU', 'NBEATSx',
+              'PatchTST', 'AutoNHITS', 'AutoNBEATS']
+    datasets = ['M3']
+    evaluation = [evaluate(model, dataset, group) for model, group in product(models, groups) for dataset in datasets]
+    evaluation = [eval_ for eval_ in evaluation if eval_ is not None]
+    evaluation = pd.concat(evaluation)
+    evaluation = evaluation[['dataset', 'model', 'time', 'mae', 'smape']]
+    evaluation['time'] /= 60 #minutes
+    evaluation = evaluation.set_index(['dataset', 'model']).stack().reset_index()
+    evaluation.columns = ['dataset', 'model', 'metric', 'val']
+    evaluation = evaluation.set_index(['dataset', 'metric', 'model']).unstack().round(3)
+    evaluation = evaluation.droplevel(0, 1).reset_index()
+    evaluation['AutoARIMA'] = [666.82, 15.35, 3.000]
+    evaluation.to_csv('data/evaluation.csv')
+    print(evaluation.T)

action_files/test_models/src/models.py

+import os
+import time
+
+import fire
+import numpy as np
+import pandas as pd
+import pytorch_lightning as pl
+import torch
+
+import neuralforecast
+from neuralforecast.core import NeuralForecast
+
+from neuralforecast.models.gru import GRU
+from neuralforecast.models.rnn import RNN
+from neuralforecast.models.tcn import TCN
+from neuralforecast.models.lstm import LSTM
+from neuralforecast.models.dilated_rnn import DilatedRNN
+from neuralforecast.models.deepar import DeepAR
+from neuralforecast.models.mlp import MLP
+from neuralforecast.models.nhits import NHITS
+from neuralforecast.models.nbeats import NBEATS
+from neuralforecast.models.nbeatsx import NBEATSx
+from neuralforecast.models.tft import TFT
+from neuralforecast.models.vanillatransformer import VanillaTransformer
+from neuralforecast.models.informer import Informer
+from neuralforecast.models.autoformer import Autoformer
+from neuralforecast.models.patchtst import PatchTST
+from neuralforecast.models.dlinear import DLinear
+
+from neuralforecast.auto import (
+    AutoMLP, AutoNHITS, AutoNBEATS, AutoDilatedRNN, AutoTFT
+)
+
+from neuralforecast.losses.pytorch import SMAPE, MAE
+from ray import tune
+
+from src.data import get_data
+
+os.environ['NIXTLA_ID_AS_COL'] = '1'
+
+
+def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
+    train, horizon, freq, seasonality = get_data('data/', dataset, group)
+    train['ds'] = pd.to_datetime(train['ds'])
+
+    config_nbeats = {
+        "input_size": tune.choice([2 * horizon]),
+        "max_steps": 1000,
+        "val_check_steps": 300,
+        "scaler_type": "minmax1",
+        "random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
+    }
+    config = {
+        "hidden_size": tune.choice([256, 512]),
+        "num_layers": tune.choice([2, 4]),
+        "input_size": tune.choice([2 * horizon]),
+        "max_steps": 1000,
+        "val_check_steps": 300,
+        "scaler_type": "minmax1",
+        "random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
+    }
+    config_drnn = {'input_size': tune.choice([2 * horizon]),
+                   'encoder_hidden_size': tune.choice([124]),
+                   "max_steps": 300,
+                   "val_check_steps": 100,
+                   "random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),}
+    models = [
+        AutoDilatedRNN(h=horizon, loss=MAE(), config=config_drnn, num_samples=2, cpus=1),
+        RNN(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
+        TCN(h=horizon, input_size=2 * horizon, encoder_hidden_size=20, max_steps=300),
+        NHITS(h=horizon, input_size=2 * horizon, dropout_prob_theta=0.5, loss=MAE(), max_steps=1000, val_check_steps=500),
+        AutoMLP(h=horizon, loss=MAE(), config=config, num_samples=2, cpus=1),
+        DLinear(h=horizon, input_size=2 * horizon, loss=MAE(), max_steps=2000, val_check_steps=500),
+        TFT(h=horizon, input_size=2 * horizon, loss=SMAPE(), hidden_size=64, scaler_type='robust', windows_batch_size=512, max_steps=1500, val_check_steps=500),
+        VanillaTransformer(h=horizon, input_size=2 * horizon, loss=MAE(), hidden_size=64, scaler_type='minmax1', windows_batch_size=512, max_steps=1500, val_check_steps=500),
+        DeepAR(h=horizon, input_size=2 * horizon, scaler_type='minmax1', max_steps=1000),
+    ]
+
+    # Models
+    for model in models[:-1]:
+        model_name = type(model).__name__
+        print(50*'-', model_name, 50*'-')
+        start = time.time()
+        fcst = NeuralForecast(models=[model], freq=freq)
+        fcst.fit(train)
+        forecasts = fcst.predict()
+        end = time.time()
+        print(end - start)
+
+        forecasts.columns = ['unique_id', 'ds', model_name]
+        forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
+        time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
+        time_df.to_csv(f'data/{model_name}-time-{dataset}-{group}.csv', index=False)
+
+    # DeepAR
+    model_name = type(models[-1]).__name__
+    start = time.time()
+    fcst = NeuralForecast(models=[models[-1]], freq=freq)
+    fcst.fit(train)
+    forecasts = fcst.predict()
+    end = time.time()
+    print(end - start)
+
+    forecasts = forecasts[['unique_id', 'ds', 'DeepAR-median']]
+    forecasts.columns = ['unique_id', 'ds', 'DeepAR']
+    forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
+    time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
+    time_df.to_csv(f'data/{model_name}-time-{dataset}-{group}.csv', index=False)
+
+
+if __name__ == '__main__':
+    fire.Fire(main)

action_files/test_models/src/models2.py

+import os
+import time
+
+import fire
+import numpy as np
+import pandas as pd
+import pytorch_lightning as pl
+import torch
+
+import neuralforecast
+from neuralforecast.core import NeuralForecast
+
+from neuralforecast.models.gru import GRU
+from neuralforecast.models.rnn import RNN
+from neuralforecast.models.tcn import TCN
+from neuralforecast.models.lstm import LSTM
+from neuralforecast.models.dilated_rnn import DilatedRNN
+from neuralforecast.models.deepar import DeepAR
+from neuralforecast.models.mlp import MLP
+from neuralforecast.models.nhits import NHITS
+from neuralforecast.models.nbeats import NBEATS
+from neuralforecast.models.nbeatsx import NBEATSx
+from neuralforecast.models.tft import TFT
+from neuralforecast.models.vanillatransformer import VanillaTransformer
+from neuralforecast.models.informer import Informer
+from neuralforecast.models.autoformer import Autoformer
+from neuralforecast.models.patchtst import PatchTST
+
+from neuralforecast.auto import (
+    AutoMLP, AutoNHITS, AutoNBEATS, AutoDilatedRNN, AutoTFT
+)
+
+from neuralforecast.losses.pytorch import SMAPE, MAE
+from ray import tune
+
+from src.data import get_data
+
+os.environ['NIXTLA_ID_AS_COL'] = '1'
+
+
+def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
+    train, horizon, freq, seasonality = get_data('data/', dataset, group)
+    train['ds'] = pd.to_datetime(train['ds'])
+
+    config_nbeats = {
+        "input_size": tune.choice([2 * horizon]),
+        "max_steps": 1000,
+        "val_check_steps": 300,
+        "scaler_type": "minmax1",
+        "random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
+    }
+    config = {
+        "hidden_size": tune.choice([256, 512]),
+        "num_layers": tune.choice([2, 4]),
+        "input_size": tune.choice([2 * horizon]),
+        "max_steps": 1000,
+        "val_check_steps": 300,
+        "scaler_type": "minmax1",
+        "random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
+    }
+    config_drnn = {'input_size': tune.choice([2 * horizon]),
+                   'encoder_hidden_size': tune.choice([124]),
+                   "max_steps": 300,
+                   "val_check_steps": 100,
+                   "random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),}
+    models = [
+        LSTM(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
+        DilatedRNN(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
+        GRU(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
+        AutoNBEATS(h=horizon, loss=MAE(), config=config_nbeats, num_samples=2, cpus=1),
+        AutoNHITS(h=horizon, loss=MAE(), config=config_nbeats, num_samples=2, cpus=1),
+        NBEATSx(h=horizon, input_size=2 * horizon, loss=MAE(), max_steps=1000),
+        PatchTST(h=horizon, input_size=2 * horizon, patch_len=4, stride=4, loss=MAE(), scaler_type='minmax1', windows_batch_size=512, max_steps=1000, val_check_steps=500),
+    ]
+
+    # Models
+    for model in models[:-1]:
+        model_name = type(model).__name__
+        print(50*'-', model_name, 50*'-')
+        start = time.time()
+        fcst = NeuralForecast(models=[model], freq=freq)
+        fcst.fit(train)
+        forecasts = fcst.predict()
+        end = time.time()
+        print(end - start)
+
+        forecasts.columns = ['unique_id', 'ds', model_name]
+        forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
+        time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
+        time_df.to_csv(f'data/{model_name}-time-{dataset}-{group}.csv', index=False)
+
+
+if __name__ == '__main__':
+    fire.Fire(main)