Time-series-prediction

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tfts: Time Series Deep Learning Models in TensorFlow

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- AGENTS.md
- review-rules.md
- - build.sh
  - meta.yaml
- - bug-report.md
  - feature-request.md
  - question.md
- - lint.yml
  - pypi_release.yml
  - test.yml
- PULL_REQUEST_TEMPLATE.md
- - __init__.py
  - base.py
  - grocery_sales.py
  - legacy_examples.py
  - recruit_restaurant.py
  - synthetic.py
- __init__.py
- base.py
- cli.py
- formatter.py
- metrics.py
- README.md
- registry.py
- runner.py
- Dockerfile
- - api.rst
  - conf.py
  - faq.rst
  - feature_engineering.rst
  - index.rst
  - installation.rst
  - models.rst
  - tricks.rst
  - tutorials.rst
- make.bat
- Makefile
- requirements_docs.txt
- - deepar_ar_demo.ipynb
  - multi_steps_sales_prediction.ipynb
  - nbeats_ar_demo.ipynb
  - single_step_stock_prediction.ipynb
  - single_step_weather_prediction.ipynb
  - tft_stallion_prediction.ipynb
- README.md
- run_anomaly.py
- run_classification.py
- run_prediction_simple.py
- run_tuner.py
- - __init__.py
  - test_get_data.py
  - test_processor.py
  - test_timeseries.py
- - __init__.py
  - test_anomaly.py
  - test_classification.py
  - test_gpu_tpus.py
  - test_prediction.py
  - test_tfts_inputs.py
  - test_uncertainty.py
- - __init__.py
  - test_auto_feature.py
  - test_datetime_feature.py
  - test_norm.py
  - test_one_order_feature.py
  - test_registry.py
  - test_two_order_feature.py
- - __init__.py
  - test_attention_layer.py
  - test_autoformer_layer.py
  - test_cnn_layer.py
  - test_dense_layer.py
  - test_embed_layer.py
  - test_graph_layer.py
  - test_mask_layer.py
  - test_moe_layer.py
  - test_nbeats_layer.py
  - test_position_layer.py
  - test_rwkv_layer.py
  - test_unet_layer.py
- - test_loss.py
- - __init__.py
  - test_auto_config.py
  - test_auto_model.py
  - test_autoformer.py
  - test_base.py
  - test_bert.py
  - test_deepar.py
  - test_diffusion.py
  - test_dlinear.py
  - test_gpt.py
  - test_informer.py
  - test_itransformer.py
  - test_nbeats.py
  - test_patch_tst.py
  - test_rnn.py
  - test_rwkv.py
  - test_seq2seq.py
  - test_tcn.py
  - test_tft.py
  - test_tide.py
  - test_transformer.py
  - test_unet.py
  - test_wavenet.py
- - __init__.py
  - test_base.py
- - __init__.py
  - test_auto_task.py
  - test_base.py
  - test_pipeline.py
- __init__.py
- test_benchmark_formatter.py
- test_constants.py
- test_demo.py
- test_generator.py
- test_init.py
- test_metrics.py
- test_serving.py
- test_trainer.py
- test_tuner.py
- - __init__.py
  - forecasting.py
- - __init__.py
  - ar.py
  - auto_preprocessor.py
  - get_data.py
  - processor.py
  - timeseries.py
- - __init__.py
  - base.py
  - normal.py
  - outputs.py
- - __init__.py
  - auto_feature.py
  - datetime_feature.py
  - norm.py
  - one_order_feature.py
  - registry.py
  - two_order_feature.py
- - __init__.py
  - autoregressive.py
  - configuration.py
  - outputs.py
  - schedules.py
- - __init__.py
  - attention_layer.py
  - autoformer_layer.py
  - cnn_layer.py
  - dense_layer.py
  - embed_layer.py
  - graph_layer.py
  - mask_layer.py
  - moe_layer.py
  - nbeats_layer.py
  - position_layer.py
  - rwkv_layer.py
  - unet_layer.py
  - util_layer.py
- - __init__.py
  - loss.py
- - __init__.py
  - auto_config.py
  - auto_model.py
  - autoformer.py
  - base.py
  - bert.py
  - deep_ar.py
  - diffusion.py
  - dlinear.py
  - gpt.py
  - informer.py
  - itransformer.py
  - nbeats.py
  - patch_tst.py
  - pfn.py
  - registry.py
  - rnn.py
  - rwkv.py
  - seq2seq.py
  - tcn.py
  - tft.py
  - tide.py
  - timemixer.py
  - timesfm.py
  - transformer.py
  - unet.py
  - wavenet.py
- - __init__.py
  - auto_task.py
  - base.py
  - pipeline.py
- - __init__.py
  - runtime.py
- - __init__.py
  - optuna_tuner.py
- __init__.py
- constants.py
- generator.py
- metrics.py
- trainer.py
- training_args.py
.gitignore
.pre-commit-config.yaml
.readthedocs.yml
AGENTS.md
CHANGELOG.md
codecov.yml
CONTRIBUTING.md
LICENSE
Makefile
poetry.lock
pyproject.toml
README.md
README_CN.md
requirements.txt
setup.cfg

# Installation Guide

1. Get the code

git clone https://github.com/LongxingTan/Time-series-prediction

Downloads the entire project code from GitHub to your computer.

cd Time-series-prediction

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install tfts

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Docker

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Docker Desktop Needed to build and run containers. Install it and keep it running in the background.

docker build -f docker/Dockerfile -t time-series-prediction .

Builds a runnable image based on the Dockerfile.

docker run -p 8080:80 time-series-prediction

Runs the built image as an actual container.

✅ Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.

4. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install tfts

Installs the package published on PyPI directly — no need to clone the source.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

5. Make

Medium

Prerequisites

Git Needed to download the project code from GitHub.
Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).

make

Compiles the code based on the generated build configuration to produce an executable.

✅ If it finishes without errors, it worked. Try running the generated executable directly.