From 3c8f1ecf66c2283e71ffff7cd6f993d9f005b9dc Mon Sep 17 00:00:00 2001 From: E33605 Date: Mon, 26 Aug 2024 12:17:23 +0530 Subject: [PATCH] =?UTF-8?q?=F0=9F=92=A3=20removing=20ts=5Futils=20from=20r?= =?UTF-8?q?td=20docs?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - #21, as code is moved to sharkutilities/pandas-wizard#24 --- docs/index.md | 1 - docs/ts_utils.md | 74 ------------------------------------------------ 2 files changed, 75 deletions(-) delete mode 100644 docs/ts_utils.md diff --git a/docs/index.md b/docs/index.md index 97a5aa7..b1b627a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -23,7 +23,6 @@ dt_utils.md prettify.md sqlparser.md -ts_utils.md ``` Welcome to the repository of self-developed Python utility functions aimed at streamlining data science, machine learning diff --git a/docs/ts_utils.md b/docs/ts_utils.md deleted file mode 100644 index 541f9f3..0000000 --- a/docs/ts_utils.md +++ /dev/null @@ -1,74 +0,0 @@ -
- -# Time Series Utilities - -✨ *stationarity, moving average, feature engineering for ts-model development* ✨ - -```{eval-rst} -.. attention:: - Source Code: `GH/ts_utils `_ -``` - -
- -
- -Feature engineering, time series stationarity checks are few of the use-cases that are compiled in this gists. Check -individual module defination and functionalities as follows. - -## Stationarity & Unit Roots - -Stationarity is one of the fundamental concepts in time series analysis. The **time series data model works on the principle that the [_data is stationary_](https://www.analyticsvidhya.com/blog/2021/04/how-to-check-stationarity-of-data-in-python/) and [_data has no unit roots_](https://www.analyticsvidhya.com/blog/2018/09/non-stationary-time-series-python/)**, this means: - * the data must have a constant mean (across all periods), - * the data should have a constant variance, and - * auto-covariance should not be dependent on time. - -Let's understand the concept using the following example, for more information check [this link](https://www.analyticsvidhya.com/blog/2018/09/non-stationary-time-series-python/). - -![Non-Stationary Time Series](https://cdn.analyticsvidhya.com/wp-content/uploads/2018/09/ns5-e1536673990684.png) - -
- -| ADF Test | KPSS Test | Series Type | Additional Steps | -| :---: | :---: | :---: | --- | -| ✅ | ✅ | _stationary_ | | -| ❌ | ❌ | _non-stationary_ | | -| ✅ | ❌ | _difference-stationary_ | Use differencing to make series stationary. | -| ❌ | ✅ | _trend-stationary_ | Remove trend to make the series _strict stationary. | - -
- -```{eval-rst} -.. automodule:: stationarity - :members: - :undoc-members: - :show-inheritance: -``` - -## Time Series Featuring - -Time series analysis is a special segment of AI/ML application development where a feature is dependent on time. The code here is desgined to create a *sequence* of `x` and `y` data needed in a time series problem. The function is defined with two input parameters (I) **Lootback Period (T) `n_lookback`**, and (II) **Forecast Period (H) `n_forecast`** which can be visually presented below. - -
- -![prediction-sequence](https://i.stack.imgur.com/YXwMJ.png) - -
- -```{eval-rst} -.. automodule:: ts_featuring - :members: - :undoc-members: - :show-inheritance: -``` - -## Time Series Models - -```{eval-rst} -.. automodule:: ts_models - :members: - :undoc-members: - :show-inheritance: -``` - -