|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "id": "c4b857d5-ca08-4681-8ac7-f8717909fa68", |
| 6 | + "metadata": {}, |
| 7 | + "source": [ |
| 8 | + "# Change detection in time serie: a small example" |
| 9 | + ] |
| 10 | + }, |
| 11 | + { |
| 12 | + "cell_type": "markdown", |
| 13 | + "id": "4a4a9e99-53bb-4d92-b227-e944d93585ab", |
| 14 | + "metadata": {}, |
| 15 | + "source": [ |
| 16 | + "## Sources/documentation\n", |
| 17 | + "* Change point detection example with various technics [from forecastegy](https://forecastegy.com/posts/change-point-detection-time-series-python/)\n", |
| 18 | + "* Cherry-tree blossom date in kyoto dataset: [download here](https://ourworldindata.org/grapher/date-of-the-peak-cherry-tree-blossom-in-kyoto)\n", |
| 19 | + "* Sample of bayesian reasoning on switch detection: [Notebook sample from the Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb)" |
| 20 | + ] |
| 21 | + }, |
| 22 | + { |
| 23 | + "cell_type": "code", |
| 24 | + "execution_count": null, |
| 25 | + "id": "8bf8f020-51bb-4b35-a6ce-bcb22de6e4f1", |
| 26 | + "metadata": {}, |
| 27 | + "outputs": [], |
| 28 | + "source": [] |
| 29 | + } |
| 30 | + ], |
| 31 | + "metadata": { |
| 32 | + "kernelspec": { |
| 33 | + "display_name": "Python 3 (ipykernel)", |
| 34 | + "language": "python", |
| 35 | + "name": "python3" |
| 36 | + }, |
| 37 | + "language_info": { |
| 38 | + "codemirror_mode": { |
| 39 | + "name": "ipython", |
| 40 | + "version": 3 |
| 41 | + }, |
| 42 | + "file_extension": ".py", |
| 43 | + "mimetype": "text/x-python", |
| 44 | + "name": "python", |
| 45 | + "nbconvert_exporter": "python", |
| 46 | + "pygments_lexer": "ipython3", |
| 47 | + "version": "3.12.1" |
| 48 | + } |
| 49 | + }, |
| 50 | + "nbformat": 4, |
| 51 | + "nbformat_minor": 5 |
| 52 | +} |
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