https://github.com/ikatsov/tensor-house-basic

A collection of templates for basic enterprise data science and ML tasks
https://github.com/ikatsov/tensor-house-basic

Last synced: 11 months ago
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A collection of templates for basic enterprise data science and ML tasks

• Host: GitHub
• URL: https://github.com/ikatsov/tensor-house-basic
• Owner: ikatsov
• License: apache-2.0
• Created: 2023-12-30T13:30:18.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-30T17:57:45.000Z (over 2 years ago)
• Last Synced: 2025-04-06T14:21:55.502Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 6.27 MB
• Stars: 5
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# TensorHouse Basic Templates

A collection of templates for basic enterprise data science and ML tasks.

* Generic Regression and Classification Models

    * Neural Network with Vector Inputs ([notebook](https://github.com/ikatsov/tensor-house-basic/blob/master/regression/vector-models.ipynb))

    * Neural Network with Sequential Inputs (ConvNet, LSTM, Attention) ([notebook](https://github.com/ikatsov/tensor-house-basic/blob/master/regression/sequence-models.ipynb))

* Enterprise Time Series Analysis

   * Forecasting Using ARIMA and SARIMA (notebooks

[1](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/arima-part-1-algorithm.ipynb)

[2](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/arima-part-2-use-case.ipynb))

   * Decomposition and Forecasting using Bayesian Structural Time Series (BSTS) (notebooks

[1](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/bsts-part-1-decomposition.ipynb)

[2](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/bsts-part-2-forecasting.ipynb)

[3](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/bsts-part-3-forecasting-prophet.ipynb)

[4](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/bsts-part-4-forecasting-pymc3.ipynb))

   * Forecasting and Decomposition using Gradient Boosted Decision Trees (GBDT) ([notebook](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/gbdt-forecasting.ipynb))

   * Forecasting and Decomposition using LSTM with Attention ([notebook](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/lstm-forecasting.ipynb))

   * Forecasting and Decomposition using VAR/VEC models (notebooks

[1](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/var-part-1-forecasting-decomposition.ipynb)

[2](https://github.com/ikatsov/tensor-house-basic/blob/master/time-series/var-part-2-market-data.ipynb))