# Time Segmentation
`Tutorials` are the best documentation — [`Time Segmentation Tutorial`](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/computational/Segmentation%20Notebook.ipynb)
### Overview
The Time Segmentation Module is a powerful tool for analyzing financial time series data. It offers four main functionalities:
1. Change Point Detection
2. Regime Change Analysis
3. Comprehensive Regime Analysis (PCA-based)
Each functionality can be used for data analysis or visualization, allowing users to gain deep insights into their financial data.
### Getting Started
To use the Time Segmentation Module, first import the necessary library and authenticate:
```python
import sovai as sov
sov.token_auth(token="your_token_here")
```
```python
df_accounting = sov.data("accounting/weekly").select_stocks("mega")
```
### 1. Change Point Detection
Identify significant changes in your time series data.
#### Data Analysis
```python
df_change = df_accounting.change_point(method='data', feature="book_equity_value")
df_change.tail(10) # View the last 10 rows
df_change.attrs['stats'] # View additional statistics
```
#### Visualization
```python
df_accounting.change_point(method='plot')
```
### 2. Regime Change Analysis
Detect regime changes for a specific stock and feature.
#### Data Analysis
```python
rc_result = df_accounting.regime_change(method="data", ticker="AAPL", feature="total_revenue")
rc_result.tail(10) # View the last 10 rows
rc_result.attrs['stats'] # View additional statistics
```
#### Visualization
```python
df_accounting.regime_change(method="plot", ticker="AAPL", feature="total_revenue")
```
### 3. Comprehensive Regime Analysis (PCA-based)
Perform a PCA-based regime change analysis on multiple features for a specific stock.
#### Data Analysis
```python
pca_rc_result = df_accounting.pca_regime_change(method="data", ticker="AAPL")
pca_rc_result.tail() # View the last rows
pca_rc_result.attrs['stats'] # View additional statistics
```
#### Visualization
```python
df_accounting.pca_regime_change(method="plot", ticker="AAPL")
```
### Tips for Users
* Always check the `.attrs['stats']` of the result for additional insights and metadata.
* Use the 'plot' method for quick visual analysis and the 'data' method for detailed numerical results.
* Experiment with different features and tickers to gain comprehensive insights into your financial data.
***
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