Metadata-Version: 2.1
Name: tseuler
Version: 0.0.4.dev0
Summary: A library for Time-Series exploration, analysis & modelling.
Home-page: https://github.com/ag-ds-bubble/tseuler
Author: Achintya Gupta
Author-email: ag.ds.bubble@gmail.com
License: UNKNOWN
Description: <img style="float: right;" src="examples/logo_big.png"  width='100%'>
        
        # tseuler
        A library for Time Series exploration, analysis & modelling. This includes -
        
        
        As of now, this libray is in pre-alpha phase, i.e there is a lot of work still left before its first stable release.
        
        ### TSMAD - Time Series Mini Analysis DashBoard.
        Functionalities Include
        
            - A mini Dashboard for Time Series Analysis, with multiple variations to each kind of analysis
            - Inbuilt Freqency Variation analysis
            - Intervention Analysis (In Future) 
            
        
        ### TSSTATS - Time Series Statistical & Modelling Functions
        Functionalities Include:
        
            - Rolling Origin Framework (Currently Supports - statsmodels, sklearn, sklearn) for both multi-variate and uni-variate
            - Residual Diagnostics
            - Statistical Tests
            - Entropy Calculations
            - Intervention Analysis (In Future)
        
        ## Example
        ****
        <img style="float: right;" src="examples/example_gif.gif"  width='100%'>
        
        ## Installation
        ****
        Installation 
        ```py
        pip install tseuler
        ```
        ## Usage
        ****
        - ### Instantiating a DashBoard
            
            ```py
            import pandas as pd
            import tseuler as tse
            # Read the Time Series DataFrame
            dataDF = pd.read_csv('Raw Data/stocks_data.csv', index_col=0)
            tsmadObj = tse.TSMAD(tsdata = dataDF, data_desc = 'Stocks Data',
                             target_columns = ['close'], categorical_columns = ['Name'],
                             dt_format = '%Y-%m-%d', dt_freq = 'B',
                             how_aggregate = {'open':'first', 'high':'max', 'low':'min', 'close':'last'},
                             force_interactive = True)
            tsmadObj.get_board()
            ```
        
        `tseuler` has been built upon:-
        ****
        - pandas
        - numpy
        - panel
        - altair
        - matplotlib
        - statsmodels
        
        
        
        ## History
        ****
        <u>v0.0.4dev0 : Development Package</u>
        - Added TSMAD
        - Added TSSTATS
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
