CoronaD.py

import getData
import statsmodels
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
#from statsmodels.tsa.arima_model import ARIMA

def ShowGraph(data): # Show data Graph
    df = pd.DataFrame(data, columns = ['date' , 'value'])
    df.date = pd.to_datetime(df.date)
    df.value = pd.to_numeric(df['value'])
    df = df.set_index('date')
    g = df['value'].plot(title="Corona-Virus Daily data")
    plt = g.get_figure()
    plt.show()

def Calc_ACF_PAF(): # ACF PAF Calculation with ARIMA models
    data = pd.read_csv('./data/data.csv', header=0, index_col=0, squeeze=True)
    edit_data = data.diff(periods=10).iloc[1:]
    plot_acf(data)
    plot_pacf(data)
    plt.show()
    # p = 0, q = 1

def ARIMA():
    series = pd.read_csv('./data/data.csv', header=0, index_col=0, squeeze=True)
    order = (0,1,1)
    model = statsmodels.tsa.arima_model.ARIMA(series, order, freq='D')
    model_fit = model.fit(trend='c',full_output=True, disp=10)
    #print(model_fit.summary())
    plt = model_fit.plot_predict()
    #plt.show()
    Number_to_predict = 3
    fore = model_fit.forecast(steps=Number_to_predict) # steps : Number to predict
    #print(fore)

    # stderr, upper bound, lower bound
    time = datetime(2020, 3, 5) # start time
    for x in fore[0]:
        p_time = time + timedelta(days=Number_to_predict)
        p_time = p_time.strftime('%Y-%m-%d')
        print("{0} : {1}".format(p_time, x))
        Number_to_predict += 1
    
    
if __name__ == "__main__":
    data = getData.read_csv('list')
    #ShowGraph(data)
    #Calc_ACF_PAF()
    ARIMA()