Metadata-Version: 2.1
Name: MRN
Version: 0.1.0
Summary: Library used to Normalize numerical time-series data
Home-page: UNKNOWN
Author: Philip Pankaj
Author-email: <philip.pankaj@gmail.com>
License: UNKNOWN
Description: # Memory Retention Normalization - MRN
        
        This Library is used for normalizing data array with respect to long term memory retention
        
        Timeseries Data's have influence of long term memory retention on their current data
        
        This phenomenon happens in the field of finance sports and other live streaming timeseries datas
        
        Developed by Philip Pankaj (c) 2021
        
        ##Example with Nasdaq 100 data
        
        ![](https://raw.githubusercontent.com/philip-hash/MTN/main/MRN_Nasdaq_Graph.png)
        
        ```python
        #importing our MRN
        from MRN.scale import Normalization
        
        #importing all required library
        from yahoo_fin.stock_info import get_data
        from datetime import date
        
        #getting nifty data and converyting to numpy array
        now=date.today().strftime("%d/%m/%Y")  
        from_date="5/5/2006" # m/d/year
        data= get_data("^IXIC", start_date=from_date, end_date=now, index_as_date = True, interval="1d")
        data=data['close'].dropna()
        data=data.to_numpy()
        
        #initializing MRN and transforming data
        mrn=Normalization(data,0.5)
        n_data=mrn.transform()
        
        #importing matplotlib and plotting
        import matplotlib.pyplot as plt
        plt.figure(figsize=(40, 40))
        
        fig, ax1 = plt.subplots()
        ax1.set_ylabel('Nasdaq-100')
        ax1.plot(data[-1000:], color = 'tab:blue')
        
        ax2=ax1.twinx()
        ax2.set_ylabel('MRN-Normalized')
        ax2.plot(n_data[-1000:],color='tab:green')
        plt.show()
        
        ```
        
Keywords: python,normalization,standardization,long term memory,LTM,LTM Normalization
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
