Metadata-Version: 2.1
Name: TimesML
Version: 0.0.5
Summary: This package is used for time series data analysis. Correct internal errors of functions, such as Chart.lag_plot, ProcessData.save_flie, Model.AutoRegressive.
Home-page: https://github.com/leodflag/TimesML
Author: leodflag
Author-email: lovedoglion5@gmail.com
License: UNKNOWN
Description: # TimesML
        ---
        ## About
        This package was developed for time series data analysis and machine learning tasks. The aim of TimesML is to provide high-level APIs that developers and data scientists can easily model their times series data. We plan to support more machine learning models in the future. Thank you for your support, please star⭐ this project if you like.
        
        ## PypI
        https://pypi.org/project/TimesML
        ```js
        pip install TimesML
        ```
        
        ## List of module
        #### Math
        * Statistics
        
            This module contains statistics calculation function.
        
            ex. mean_square_error、coefficient_of_determination、
                ACF
        
        #### TimeSeriesAnalysis
        * Chart
        
            Time series data related drawing function.
        
            ex. statistics_infographic、ACF_chart、forecast_result_group_chart
        
        * Model
        
            This module contains time series models for forecasting.
        
            ex. AutoRegressive、SimpleMovingAverage
        
        * ProcessData
        
            This module contains the access and processing methods of time series data.
        
            ex. get_data_yahoo、n_order_difference_data、split_data
        
        #### Test Data
        Please download the file 'g20_new_c.csv'  first for the following simple example.
        https://github.com/leodflag/TimesML/tree/master/test_data
        
        Pay attention to the file path,'g20_new_c.csv' should belong to the same file level as the simple example program.
        
        ## Simple example
        ```js
        import TimeSeriesAnalysis.ProcessData as Data
        import TimeSeriesAnalysis.Model as Model
        import TimeSeriesAnalysis.Chart as Chart
        
        # setting parameters
        save_path = 'US'
        chart = Chart.chart('US')
        
        # read data
        data = Data.read_file(path='g20_new_c.csv', col_name='US')
        
        # contains basic statistics: historocal trend line chart、lag plot、ACF chart.
        chart.statistics_infographic(data, file_path=save_path, lags=20, xlabel='date', ylabel='population')
        
        # split data into training set and test set
        train, test = Data.split_data(data, ratio=0.7)
        
        # autoregressive lag periods :2
        model1 = Model.AutoRegressive(lags=2)
        model1.fit(train)
        model1.predict(test, pure_test_set_predict=True)
        
        # autoregressive lag periods :20
        model2 = Model.AutoRegressive(lags=20)
        model2.fit(train)
        model2.predict(test,pure_test_set_predict= True)
        
        # Save the data predicted by model1 using the test set
        Data.save_flie(model1.test_predict, path=save_path, stock_id='US', file_format='csv')
        
        # Combine and compare the prediction results of the two models.
        chart.forecast_result_group_chart(train, test, model1, model2, file_path=save_path, 
        model_1_name='AR(2)', model_2_name='AR(20)', xlabel='date', ylabel='population')
        
        # simple moving average. five days as a unit to calculate the average
        model3 = Model.SimpleMovingAverage(windows=5)
        model3.fit(data)
        
        # line chart to observe the average situation every five days.
        chart.line_chart(data, model3.sma_result, chart_title='SMA(5)', file_path=save_path, xlabel='date', ylabel='population')
        ```
        
        ## TimesML (github)
        https://github.com/leodflag/TimesML
        
        ## MIT License
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
