
# import libraries
import datetime

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.model_selection import train_test_split, TimeSeriesSplit

import warnings
warnings.filterwarnings('ignore')


import itertools
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARIMA


# import datasets
cases_df = pd.read_csv('../clean_data/Cases_Normalized_per_100k.csv')
cases_df.head()


cases_df.describe()


# convert date to datetime
cases_df['date'] = pd.to_datetime(cases_df['date'])


# drop no longer needed columns
cases_df.drop(['totalcountconfirmed', 'totalcountdeaths', 'newcountconfirmed',
              'newcountdeaths', 'NEVER', 'RARELY', 'SOMETIMES', 'FREQUENTLY', 
              'ALWAYS', 'deathper100k', 'newdeath100k', 'confirmedper100k', 'population',
               'newlyconfirmedper100k' ,'7dayrollingavg_newlyconfirmed_rawnumber', '7dayrollingavg_newdeath_rawnumber'], 
             axis = 1, inplace = True)
cases_df.head()


hospitals_df = pd.read_csv('../clean_data/hospitals_by_county.csv')
hospitals_df.head()


hospitals_df['todays_date'] = pd.to_datetime(hospitals_df['todays_date'])


hospitals_df.drop(['hospitalized_covid_confirmed_patients', 'hospitalized_suspected_covid_patients', 
                   'icu_covid_confirmed_patients', 'icu_suspected_covid_patients'],
                 axis = 1, inplace = True)
hospitals_df.head()


cases_hosp_df = cases_df.merge(hospitals_df, how = 'inner', left_on = ['county','date'], right_on = ['county', 'todays_date'])
cases_hosp_df.head()


# do a group by so we can create predictions per county
county_df = cases_hosp_df.groupby(['county', 'date']).mean()


county_df.index.get_level_values('county').unique()[0]

'Alameda'


# start by analyzing LA County
county_df.loc['Los Angeles']


county_df.loc['Los Angeles']


la_df = county_df.loc['Los Angeles'].tail(110)
features = ['hospitalized_covid_patients', '7dayrollingavg_newdeath', 'all_hospital_beds', 'icu_available_beds']
X = la_df[features]
y = la_df[['7dayrollingavg_newlyconfirmed']]


X_train = X.iloc[:92]
X_test = X.iloc[92:]
y_train = y.iloc[:92]
y_test = y.iloc[92:]
X_train.tail()


%pip install pmdarima

Collecting pmdarima
  Downloading pmdarima-1.8.0-cp38-cp38-manylinux1_x86_64.whl (1.5 MB)
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Requirement already satisfied: scikit-learn>=0.22 in /home/jesse/anaconda3/lib/python3.8/site-packages (from pmdarima) (0.24.1)
Collecting Cython<0.29.18,>=0.29
  Downloading Cython-0.29.17-cp38-cp38-manylinux1_x86_64.whl (2.0 MB)
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Installing collected packages: Cython, pmdarima
  Attempting uninstall: Cython
    Found existing installation: Cython 0.29.21
    Uninstalling Cython-0.29.21:
      Successfully uninstalled Cython-0.29.21
Successfully installed Cython-0.29.17 pmdarima-1.8.0
Note: you may need to restart the kernel to use updated packages.


import pmdarima as pm


smodel = pm.auto_arima(y_train, start_p=1, start_q=1,
                         test='adf',
                         max_p=3, max_q=3, m=12,
                         start_P=0, seasonal=False,
                         d=None, D=None, trace=True,
                         error_action='ignore',  
                         suppress_warnings=True, 
                         stepwise=True)

smodel.summary()

Performing stepwise search to minimize aic
 ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=575.359, Time=0.07 sec
 ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=649.159, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=611.739, Time=0.04 sec
 ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.10 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=647.160, Time=0.01 sec
 ARIMA(2,2,1)(0,0,0)[0] intercept   : AIC=576.989, Time=0.08 sec
 ARIMA(1,2,2)(0,0,0)[0] intercept   : AIC=576.229, Time=0.09 sec
 ARIMA(0,2,2)(0,0,0)[0] intercept   : AIC=574.601, Time=0.13 sec
 ARIMA(0,2,3)(0,0,0)[0] intercept   : AIC=576.291, Time=0.08 sec
 ARIMA(1,2,3)(0,0,0)[0] intercept   : AIC=577.940, Time=0.13 sec
 ARIMA(0,2,2)(0,0,0)[0]             : AIC=572.608, Time=0.06 sec
 ARIMA(0,2,1)(0,0,0)[0]             : AIC=575.638, Time=0.02 sec
 ARIMA(1,2,2)(0,0,0)[0]             : AIC=574.247, Time=0.06 sec
 ARIMA(0,2,3)(0,0,0)[0]             : AIC=574.308, Time=0.05 sec
 ARIMA(1,2,1)(0,0,0)[0]             : AIC=573.362, Time=0.04 sec
 ARIMA(1,2,3)(0,0,0)[0]             : AIC=575.941, Time=0.10 sec

Best model:  ARIMA(0,2,2)(0,0,0)[0]          
Total fit time: 1.148 seconds


smodel.plot_diagnostics(figsize=(16, 8))
plt.show()
plt.savefig('../images/arima_model_diags.png');

<Figure size 432x288 with 0 Axes>


warnings.filterwarnings("ignore") 

model = ARIMA(y_train, order=smodel.order)  
fitted = model.fit(disp=-1)  
print(fitted.summary())
# Forecast
fc, se, conf = fitted.forecast(18, alpha=0.05)  # 95% conf

# Make as pandas series
fc_series = pd.Series(fc, index=y_test.index)
lower_series = pd.Series(conf[:, 0], index=y_test.index)
upper_series = pd.Series(conf[:, 1], index=y_test.index)

# Plot
plt.figure(figsize=(14,5))
plt.plot(y_train, label='training')
plt.plot(y_test, label='actual')
plt.plot(fc_series, label='forecast')
plt.fill_between(lower_series.index, lower_series, upper_series, 
                 color='k', alpha=.15)
plt.title('Forecast vs Actuals')
plt.legend()
plt.show()
plt.savefig('../images/arima_la_county.png')

                                    ARIMA Model Results                                     
============================================================================================
Dep. Variable:     D2.7dayrollingavg_newlyconfirmed   No. Observations:                   90
Model:                               ARIMA(0, 2, 2)   Log Likelihood                -283.300
Method:                                     css-mle   S.D. of innovations              5.567
Date:                              Thu, 28 Jan 2021   AIC                            574.601
Time:                                      20:54:00   BIC                            584.600
Sample:                                  10-09-2020   HQIC                           578.633
                                       - 01-06-2021                                         
==========================================================================================================
                                             coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------------
const                                     -0.0050      0.059     -0.085      0.932      -0.121       0.111
ma.L1.D2.7dayrollingavg_newlyconfirmed    -1.1886      0.110    -10.791      0.000      -1.405      -0.973
ma.L2.D2.7dayrollingavg_newlyconfirmed     0.2793      0.118      2.377      0.017       0.049       0.510
                                    Roots                                    
=============================================================================
                  Real          Imaginary           Modulus         Frequency
-----------------------------------------------------------------------------
MA.1            1.1545           +0.0000j            1.1545            0.0000
MA.2            3.1012           +0.0000j            3.1012            0.0000
-----------------------------------------------------------------------------

<Figure size 432x288 with 0 Axes>


print('MSE of our forecast is:', mean_squared_error(y_test, fc_series, squared=True))
print('RMSE of our forecast is:', mean_squared_error(y_test, fc_series, squared=False))

MSE of our forecast is: 524.1636509856758
RMSE of our forecast is: 22.894620568720413


smodel = pm.auto_arima(y, start_p=1, start_q=1,
                         test='adf',
                         max_p=3, max_q=3, m=12,
                         start_P=0, seasonal=False,
                         d=None, D=None, trace=True,
                         error_action='ignore',  
                         suppress_warnings=True, 
                         stepwise=True)

smodel.summary()

Performing stepwise search to minimize aic
 ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=684.987, Time=0.11 sec
 ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=761.359, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=718.901, Time=0.06 sec
 ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=686.186, Time=0.07 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=759.361, Time=0.01 sec
 ARIMA(2,2,1)(0,0,0)[0] intercept   : AIC=686.897, Time=0.11 sec
 ARIMA(1,2,2)(0,0,0)[0] intercept   : AIC=686.522, Time=0.12 sec
 ARIMA(0,2,2)(0,0,0)[0] intercept   : AIC=684.739, Time=0.05 sec
 ARIMA(0,2,3)(0,0,0)[0] intercept   : AIC=686.600, Time=0.06 sec
 ARIMA(1,2,3)(0,0,0)[0] intercept   : AIC=687.512, Time=0.14 sec
 ARIMA(0,2,2)(0,0,0)[0]             : AIC=683.386, Time=0.04 sec
 ARIMA(0,2,1)(0,0,0)[0]             : AIC=684.809, Time=0.03 sec
 ARIMA(1,2,2)(0,0,0)[0]             : AIC=685.147, Time=0.07 sec
 ARIMA(0,2,3)(0,0,0)[0]             : AIC=685.232, Time=0.07 sec
 ARIMA(1,2,1)(0,0,0)[0]             : AIC=683.639, Time=0.05 sec
 ARIMA(1,2,3)(0,0,0)[0]             : AIC=686.161, Time=0.17 sec

Best model:  ARIMA(0,2,2)(0,0,0)[0]          
Total fit time: 1.201 seconds


# Forecast
n_periods = 3
fitted, confint = smodel.predict(n_periods=n_periods, return_conf_int=True)
index_of_fc = pd.date_range(y.index[-1], periods=n_periods, freq='W')

# make series for plotting purpose
fitted_series = pd.Series(fitted, index=index_of_fc)
lower_series = pd.Series(confint[:, 0], index=index_of_fc)
upper_series = pd.Series(confint[:, 1], index=index_of_fc)

# Plot

fig = plt.figure(figsize=(14, 5))

plt.plot(y, label="Actual")
plt.plot(fitted_series, color='orange', label='Forecast')
plt.fill_between(lower_series.index,
                 lower_series,
                 upper_series,
                 color='k', alpha=.1)

plt.title('Final Forecast')
plt.legend()
plt.show()


hotspot_counties = ['Tehama', 'Riverside', 'Colusa', 'Merced', 'Santa Barbara', 'Inyo', 'Tulare']

features = ['hospitalized_covid_patients', '7dayrollingavg_newdeath', 'all_hospital_beds', 'icu_available_beds']


## Tehama County

Tehama = county_df.loc['Tehama'].tail(110)
Riverside = county_df.loc['Riverside'].tail(110)
Colusa = county_df.loc['Colusa'].tail(110)
Merced = county_df.loc['Merced'].tail(110)
Santa_Barbara = county_df.loc['Santa Barbara'].tail(110)
Inyo = county_df.loc['Inyo'].tail(110)
Tulare = county_df.loc['Tulare'].tail(110)


# build a function to incorporate everything 
warnings.filterwarnings("ignore") 
def arima_model (countydf):
    X = countydf[features]
    y = countydf[['7dayrollingavg_newlyconfirmed']]   
    X_train = X.iloc[:92]
    X_test = X.iloc[92:]
    y_train = y.iloc[:92]
    y_test = y.iloc[92:]
    
    smodel = pm.auto_arima(y_train, start_p=1, start_q=1,
                         test='adf',
                         max_p=3, max_q=3, m=12,
                         start_P=0, seasonal=False,
                         d=None, D=None, trace=True,
                         error_action='ignore',  
                         suppress_warnings=True, 
                         stepwise=True)

    print(smodel.summary())

    model = ARIMA(y_train, order=smodel.order)  
    fitted = model.fit(disp=-1)  
    print(fitted.summary())
    # Forecast
    fc, se, conf = fitted.forecast(18, alpha=0.05)  # 95% conf

    # Make as pandas series
    fc_series = pd.Series(fc, index=y_test.index)
    lower_series = pd.Series(conf[:, 0], index=y_test.index)
    upper_series = pd.Series(conf[:, 1], index=y_test.index)

    # Plot
    plt.figure(figsize=(14,5))
    plt.plot(y_train, label='training')
    plt.plot(y_test, label='actual')
    plt.plot(fc_series, label='forecast')
    plt.fill_between(lower_series.index, lower_series, upper_series, 
                     color='k', alpha=.15)
    plt.title('Forecast vs Actuals')
    plt.legend()
    plt.show()



    print('MSE of our forecast is:', mean_squared_error(y_test, fc_series, squared=True))
    print('RMSE of our forecast is:', mean_squared_error(y_test, fc_series, squared=False))


    
    # use the whole dataset to make actual predictions
    
    smodel = pm.auto_arima(y, start_p=1, start_q=1,
                             test='adf',
                             max_p=3, max_q=3, m=12,
                             start_P=0, seasonal=False,
                             d=None, D=None, trace=True,
                             error_action='ignore',  
                             suppress_warnings=True, 
                             stepwise=True)

    print(smodel.summary())


    # Forecast
    n_periods = 3
    fitted, confint = smodel.predict(n_periods=n_periods, return_conf_int=True)
    index_of_fc = pd.date_range(y.index[-1], periods=n_periods, freq='W')

    # make series for plotting purpose
    fitted_series = pd.Series(fitted, index=index_of_fc)
    lower_series = pd.Series(confint[:, 0], index=index_of_fc)
    upper_series = pd.Series(confint[:, 1], index=index_of_fc)

    # Plot

    fig = plt.figure(figsize=(14, 5))

    plt.plot(y, label="Actual")
    plt.plot(fitted_series, color='orange', label='Forecast')
    plt.fill_between(lower_series.index,
                     lower_series,
                     upper_series,
                     color='k', alpha=.1)

    plt.title('Final Forecast')
    plt.legend()
    plt.show()
    print(fitted_series)
    return


arima_model(Tehama)

Performing stepwise search to minimize aic
 ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec
 ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=596.915, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=567.628, Time=0.03 sec
 ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.10 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=594.944, Time=0.01 sec
 ARIMA(2,2,0)(0,0,0)[0] intercept   : AIC=554.762, Time=0.04 sec
 ARIMA(3,2,0)(0,0,0)[0] intercept   : AIC=547.391, Time=0.07 sec
 ARIMA(3,2,1)(0,0,0)[0] intercept   : AIC=543.793, Time=0.09 sec
 ARIMA(2,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.19 sec
 ARIMA(3,2,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.26 sec
 ARIMA(2,2,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.15 sec
 ARIMA(3,2,1)(0,0,0)[0]             : AIC=542.016, Time=0.05 sec
 ARIMA(2,2,1)(0,0,0)[0]             : AIC=inf, Time=0.16 sec
 ARIMA(3,2,0)(0,0,0)[0]             : AIC=545.566, Time=0.03 sec
 ARIMA(3,2,2)(0,0,0)[0]             : AIC=inf, Time=0.22 sec
 ARIMA(2,2,0)(0,0,0)[0]             : AIC=552.906, Time=0.02 sec
 ARIMA(2,2,2)(0,0,0)[0]             : AIC=inf, Time=0.10 sec

Best model:  ARIMA(3,2,1)(0,0,0)[0]          
Total fit time: 1.684 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                   92
Model:               SARIMAX(3, 2, 1)   Log Likelihood                -266.008
Date:                Thu, 28 Jan 2021   AIC                            542.016
Time:                        20:54:23   BIC                            554.515
Sample:                             0   HQIC                           547.056
                                 - 92                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4776      0.325     -1.472      0.141      -1.114       0.159
ar.L2         -0.3546      0.216     -1.638      0.101      -0.779       0.070
ar.L3         -0.1883      0.184     -1.024      0.306      -0.549       0.172
ma.L1         -0.5083      0.262     -1.937      0.053      -1.023       0.006
sigma2        21.3068      3.318      6.422      0.000      14.804      27.809
===================================================================================
Ljung-Box (L1) (Q):                   0.06   Jarque-Bera (JB):                 2.71
Prob(Q):                              0.80   Prob(JB):                         0.26
Heteroskedasticity (H):               7.35   Skew:                            -0.42
Prob(H) (two-sided):                  0.00   Kurtosis:                         3.17
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
                                    ARIMA Model Results                                     
============================================================================================
Dep. Variable:     D2.7dayrollingavg_newlyconfirmed   No. Observations:                   90
Model:                               ARIMA(3, 2, 1)   Log Likelihood                -265.896
Method:                                     css-mle   S.D. of innovations              4.610
Date:                              Thu, 28 Jan 2021   AIC                            543.793
Time:                                      20:54:23   BIC                            558.792
Sample:                                  10-09-2020   HQIC                           549.841
                                       - 01-06-2021                                         
==========================================================================================================
                                             coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------------
const                                      0.0570      0.121      0.471      0.638      -0.180       0.294
ar.L1.D2.7dayrollingavg_newlyconfirmed    -0.4783      0.204     -2.342      0.019      -0.879      -0.078
ar.L2.D2.7dayrollingavg_newlyconfirmed    -0.3548      0.185     -1.914      0.056      -0.718       0.009
ar.L3.D2.7dayrollingavg_newlyconfirmed    -0.1883      0.143     -1.313      0.189      -0.469       0.093
ma.L1.D2.7dayrollingavg_newlyconfirmed    -0.5090      0.194     -2.624      0.009      -0.889      -0.129
                                    Roots                                    
=============================================================================
                  Real          Imaginary           Modulus         Frequency
-----------------------------------------------------------------------------
AR.1            0.0410           -1.6429j            1.6434           -0.2460
AR.2            0.0410           +1.6429j            1.6434            0.2460
AR.3           -1.9658           -0.0000j            1.9658           -0.5000
MA.1            1.9647           +0.0000j            1.9647            0.0000
-----------------------------------------------------------------------------

MSE of our forecast is: 3665.445480922672
RMSE of our forecast is: 60.5429226328121
Performing stepwise search to minimize aic
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=653.938, Time=0.07 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=654.595, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=652.013, Time=0.04 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=652.508, Time=0.06 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=653.046, Time=0.01 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=653.909, Time=0.07 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=655.115, Time=0.16 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=650.691, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=652.523, Time=0.03 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=652.574, Time=0.04 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=651.216, Time=0.03 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=653.454, Time=0.10 sec

Best model:  ARIMA(1,1,0)(0,0,0)[0]          
Total fit time: 0.653 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  110
Model:               SARIMAX(1, 1, 0)   Log Likelihood                -323.346
Date:                Thu, 28 Jan 2021   AIC                            650.691
Time:                        20:54:24   BIC                            656.074
Sample:                             0   HQIC                           652.874
                                - 110                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.1970      0.093     -2.118      0.034      -0.379      -0.015
sigma2        22.0807      2.690      8.210      0.000      16.809      27.352
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 1.64
Prob(Q):                              0.99   Prob(JB):                         0.44
Heteroskedasticity (H):               6.74   Skew:                            -0.14
Prob(H) (two-sided):                  0.00   Kurtosis:                         3.54
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

2021-01-24    49.300240
2021-01-31    49.317279
2021-02-07    49.313922
Freq: W-SUN, dtype: float64


## Riverside County

arima_model(Riverside)

Performing stepwise search to minimize aic
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=563.188, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=581.413, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=561.905, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=564.721, Time=0.03 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=584.545, Time=0.01 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=562.932, Time=0.05 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=564.921, Time=0.07 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=561.854, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=563.251, Time=0.02 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=563.352, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=566.089, Time=0.02 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=565.251, Time=0.03 sec

Best model:  ARIMA(1,1,0)(0,0,0)[0]          
Total fit time: 0.373 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                   92
Model:               SARIMAX(1, 1, 0)   Log Likelihood                -278.927
Date:                Thu, 28 Jan 2021   AIC                            561.854
Time:                        20:54:25   BIC                            566.875
Sample:                             0   HQIC                           563.880
                                 - 92                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.4864      0.069      7.084      0.000       0.352       0.621
sigma2        26.8271      2.167     12.381      0.000      22.580      31.074
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):               225.20
Prob(Q):                              0.80   Prob(JB):                         0.00
Heteroskedasticity (H):              56.29   Skew:                            -0.61
Prob(H) (two-sided):                  0.00   Kurtosis:                        10.61
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
                                    ARIMA Model Results                                    
===========================================================================================
Dep. Variable:     D.7dayrollingavg_newlyconfirmed   No. Observations:                   91
Model:                              ARIMA(1, 1, 0)   Log Likelihood                -277.953
Method:                                    css-mle   S.D. of innovations              5.125
Date:                             Thu, 28 Jan 2021   AIC                            561.905
Time:                                     20:54:25   BIC                            569.438
Sample:                                 10-08-2020   HQIC                           564.944
                                      - 01-06-2021                                         
=========================================================================================================
                                            coef    std err          z      P>|z|      [0.025      0.975]
---------------------------------------------------------------------------------------------------------
const                                     1.4125      0.981      1.440      0.150      -0.510       3.335
ar.L1.D.7dayrollingavg_newlyconfirmed     0.4571      0.092      4.943      0.000       0.276       0.638
                                    Roots                                    
=============================================================================
                  Real          Imaginary           Modulus         Frequency
-----------------------------------------------------------------------------
AR.1            2.1877           +0.0000j            2.1877            0.0000
-----------------------------------------------------------------------------

MSE of our forecast is: 1428.9265775463396
RMSE of our forecast is: 37.80114518829211
Performing stepwise search to minimize aic
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=681.099, Time=0.08 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=704.927, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=679.688, Time=0.03 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=683.758, Time=0.03 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=704.297, Time=0.01 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=680.932, Time=0.09 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=682.930, Time=0.10 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=678.200, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=679.573, Time=0.03 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=679.689, Time=0.05 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=682.697, Time=0.03 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=681.573, Time=0.06 sec

Best model:  ARIMA(1,1,0)(0,0,0)[0]          
Total fit time: 0.559 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  110
Model:               SARIMAX(1, 1, 0)   Log Likelihood                -337.100
Date:                Thu, 28 Jan 2021   AIC                            678.200
Time:                        20:54:26   BIC                            683.583
Sample:                             0   HQIC                           680.383
                                - 110                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.4745      0.061      7.716      0.000       0.354       0.595
sigma2        28.3635      2.259     12.558      0.000      23.937      32.790
===================================================================================
Ljung-Box (L1) (Q):                   0.14   Jarque-Bera (JB):               132.89
Prob(Q):                              0.71   Prob(JB):                         0.00
Heteroskedasticity (H):              35.46   Skew:                            -0.47
Prob(H) (two-sided):                  0.00   Kurtosis:                         8.33
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

2021-01-24    85.122324
2021-01-31    85.561050
2021-02-07    85.769222
Freq: W-SUN, dtype: float64


## Colusa County

arima_model(Colusa)

Performing stepwise search to minimize aic
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=637.091, Time=0.07 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=635.615, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=637.548, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=637.493, Time=0.03 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=634.508, Time=0.01 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0]          
Total fit time: 0.146 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                   92
Model:               SARIMAX(0, 1, 0)   Log Likelihood                -316.254
Date:                Thu, 28 Jan 2021   AIC                            634.508
Time:                        20:54:26   BIC                            637.019
Sample:                             0   HQIC                           635.521
                                 - 92                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
sigma2        61.1152      3.985     15.337      0.000      53.305      68.925
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):               314.85
Prob(Q):                              0.79   Prob(JB):                         0.00
Heteroskedasticity (H):              44.51   Skew:                            -0.99
Prob(H) (two-sided):                  0.00   Kurtosis:                        11.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
                                    ARIMA Model Results                                    
===========================================================================================
Dep. Variable:     D.7dayrollingavg_newlyconfirmed   No. Observations:                   91
Model:                              ARIMA(0, 1, 0)   Log Likelihood                -315.808
Method:                                        css   S.D. of innovations              7.779
Date:                             Thu, 28 Jan 2021   AIC                            635.615
Time:                                     20:54:26   BIC                            640.637
Sample:                                 10-08-2020   HQIC                           637.641
                                      - 01-06-2021                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.7723      0.815      0.947      0.344      -0.826       2.371
==============================================================================

MSE of our forecast is: 165.25195474667112
RMSE of our forecast is: 12.855036162791263
Performing stepwise search to minimize aic
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=769.384, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=768.846, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=770.750, Time=0.05 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=770.645, Time=0.06 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=767.414, Time=0.01 sec

Best model:  ARIMA(0,1,0)(0,0,0)[0]          
Total fit time: 0.235 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  110
Model:               SARIMAX(0, 1, 0)   Log Likelihood                -382.707
Date:                Thu, 28 Jan 2021   AIC                            767.414
Time:                        20:54:26   BIC                            770.105
Sample:                             0   HQIC                           768.505
                                - 110                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
sigma2        65.6456      4.422     14.846      0.000      56.979      74.312
===================================================================================
Ljung-Box (L1) (Q):                   0.10   Jarque-Bera (JB):               204.30
Prob(Q):                              0.75   Prob(JB):                         0.00
Heteroskedasticity (H):              24.38   Skew:                            -0.96
Prob(H) (two-sided):                  0.00   Kurtosis:                         9.43
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

2021-01-24    66.96325
2021-01-31    66.96325
2021-02-07    66.96325
Freq: W-SUN, dtype: float64


## Merced County## Merced County

arima_model(Merced)

Performing stepwise search to minimize aic
 ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.11 sec
 ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=558.921, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=530.665, Time=0.02 sec
 ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=556.940, Time=0.01 sec
 ARIMA(2,2,0)(0,0,0)[0] intercept   : AIC=531.169, Time=0.04 sec
 ARIMA(2,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.20 sec
 ARIMA(1,2,0)(0,0,0)[0]             : AIC=528.698, Time=0.02 sec
 ARIMA(2,2,0)(0,0,0)[0]             : AIC=529.206, Time=0.02 sec
 ARIMA(1,2,1)(0,0,0)[0]             : AIC=inf, Time=0.07 sec
 ARIMA(0,2,1)(0,0,0)[0]             : AIC=515.151, Time=0.03 sec
 ARIMA(0,2,2)(0,0,0)[0]             : AIC=inf, Time=0.06 sec
 ARIMA(1,2,2)(0,0,0)[0]             : AIC=510.706, Time=0.06 sec
 ARIMA(2,2,2)(0,0,0)[0]             : AIC=inf, Time=0.15 sec
 ARIMA(1,2,3)(0,0,0)[0]             : AIC=inf, Time=0.16 sec
 ARIMA(0,2,3)(0,0,0)[0]             : AIC=inf, Time=0.11 sec
 ARIMA(2,2,1)(0,0,0)[0]             : AIC=inf, Time=0.13 sec
 ARIMA(2,2,3)(0,0,0)[0]             : AIC=inf, Time=0.23 sec
 ARIMA(1,2,2)(0,0,0)[0] intercept   : AIC=512.639, Time=0.08 sec

Best model:  ARIMA(1,2,2)(0,0,0)[0]          
Total fit time: 1.608 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                   92
Model:               SARIMAX(1, 2, 2)   Log Likelihood                -251.353
Date:                Thu, 28 Jan 2021   AIC                            510.706
Time:                        20:54:28   BIC                            520.705
Sample:                             0   HQIC                           514.738
                                 - 92                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9857      0.051    -19.470      0.000      -1.085      -0.886
ma.L1          0.1689      0.094      1.789      0.074      -0.016       0.354
ma.L2         -0.7044      0.105     -6.734      0.000      -0.909      -0.499
sigma2        15.3459      1.557      9.853      0.000      12.293      18.398
===================================================================================
Ljung-Box (L1) (Q):                   1.64   Jarque-Bera (JB):               270.31
Prob(Q):                              0.20   Prob(JB):                         0.00
Heteroskedasticity (H):              28.81   Skew:                             0.99
Prob(H) (two-sided):                  0.00   Kurtosis:                        11.26
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
                                    ARIMA Model Results                                     
============================================================================================
Dep. Variable:     D2.7dayrollingavg_newlyconfirmed   No. Observations:                   90
Model:                               ARIMA(1, 2, 2)   Log Likelihood                -262.670
Method:                                     css-mle   S.D. of innovations              3.856
Date:                              Thu, 28 Jan 2021   AIC                            535.340
Time:                                      20:54:29   BIC                            547.839
Sample:                                  10-09-2020   HQIC                           540.380
                                       - 01-06-2021                                         
==========================================================================================================
                                             coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------------
const                                      0.0084        nan        nan        nan         nan         nan
ar.L1.D2.7dayrollingavg_newlyconfirmed    -1.0000        nan        nan        nan         nan         nan
ma.L1.D2.7dayrollingavg_newlyconfirmed    -0.0456      0.104     -0.440      0.660      -0.249       0.158
ma.L2.D2.7dayrollingavg_newlyconfirmed    -0.9544      0.097     -9.807      0.000      -1.145      -0.764
                                    Roots                                    
=============================================================================
                  Real          Imaginary           Modulus         Frequency
-----------------------------------------------------------------------------
AR.1           -1.0000           +0.0000j            1.0000            0.5000
MA.1            1.0000           +0.0000j            1.0000            0.0000
MA.2           -1.0478           +0.0000j            1.0478            0.5000
-----------------------------------------------------------------------------

	county	totalcountconfirmed	totalcountdeaths	newcountconfirmed	newcountdeaths	date	NEVER	RARELY	SOMETIMES	FREQUENTLY	ALWAYS	population	confirmedper100k	newlyconfirmedper100k	deathper100k	newdeath100k	7dayrollingavg_newlyconfirmed	7dayrollingavg_newdeath	7dayrollingavg_newlyconfirmed_rawnumber	7dayrollingavg_newdeath_rawnumber
0	Alameda	29.0	0.0	29	0	2020-03-18	0.019	0.008	0.055	0.123	0.795	1671329	1.735146	1.735146	0.000000	0.000000	NaN	NaN	NaN	NaN
1	Alameda	36.0	0.0	7	0	2020-03-19	0.019	0.008	0.055	0.123	0.795	1671329	2.153974	0.418828	0.000000	0.000000	NaN	NaN	NaN	NaN
2	Alameda	42.0	0.0	6	0	2020-03-20	0.019	0.008	0.055	0.123	0.795	1671329	2.512970	0.358996	0.000000	0.000000	NaN	NaN	NaN	NaN
3	Alameda	62.0	0.0	20	0	2020-03-21	0.019	0.008	0.055	0.123	0.795	1671329	3.709623	1.196652	0.000000	0.000000	NaN	NaN	NaN	NaN
4	Alameda	72.0	1.0	10	1	2020-03-22	0.019	0.008	0.055	0.123	0.795	1671329	4.307949	0.598326	0.059833	0.059833	NaN	NaN	NaN	NaN

	totalcountconfirmed	totalcountdeaths	newcountconfirmed	newcountdeaths	NEVER	RARELY	SOMETIMES	FREQUENTLY	ALWAYS	population	confirmedper100k	newlyconfirmedper100k	deathper100k	newdeath100k	7dayrollingavg_newlyconfirmed	7dayrollingavg_newdeath	7dayrollingavg_newlyconfirmed_rawnumber	7dayrollingavg_newdeath_rawnumber
count	1.812400e+04	18124.000000	18124.000000	18124.000000	18124.000000	18124.000000	18124.000000	18124.000000	18124.000000	1.812400e+04	18124.000000	18124.000000	18124.000000	18124.000000	17776.000000	1.777600e+04	17776.000000	17776.000000
mean	1.327444e+04	210.774443	173.013187	2.048003	0.032279	0.030699	0.063650	0.158545	0.714792	6.823606e+05	1542.530785	20.284986	19.606417	0.210988	20.232502	2.081917e-01	172.290086	2.001583
std	5.201773e+04	852.430551	814.985729	11.017366	0.028533	0.026178	0.033898	0.040410	0.092762	1.456208e+06	2084.332897	43.944282	28.624650	0.831238	31.417552	4.040341e-01	775.800926	9.438015
min	0.000000e+00	0.000000	-1157.000000	-16.000000	0.001000	0.000000	0.004000	0.058000	0.482000	1.129000e+03	0.000000	-74.546183	0.000000	-5.543545	-6.977150	-7.919349e-01	-1.142857	-1.428571
25%	9.400000e+01	1.000000	1.000000	0.000000	0.015000	0.013000	0.040000	0.134000	0.661000	4.590500e+04	99.646385	0.606875	1.002456	0.000000	2.049615	1.522592e-15	2.000000	0.000000
50%	1.044000e+03	15.000000	14.000000	0.000000	0.023000	0.023000	0.058000	0.156000	0.737000	1.928430e+05	711.532789	6.700781	8.138623	0.000000	8.310360	5.983262e-02	18.285714	0.142857
75%	8.147250e+03	114.000000	85.000000	0.000000	0.043000	0.043000	0.084000	0.186000	0.786000	7.621480e+05	2248.121154	22.165620	27.829294	0.000000	24.251025	2.301092e-01	89.464286	1.000000
max	1.048757e+06	15260.000000	28549.000000	318.000000	0.140000	0.135000	0.162000	0.276000	0.889000	1.003911e+07	15791.711641	1860.053144	272.052534	27.717723	566.793296	6.335479e+00	15711.142857	241.285714

	county	date	7dayrollingavg_newlyconfirmed	7dayrollingavg_newdeath
0	Alameda	2020-03-18	NaN	NaN
1	Alameda	2020-03-19	NaN	NaN
2	Alameda	2020-03-20	NaN	NaN
3	Alameda	2020-03-21	NaN	NaN
4	Alameda	2020-03-22	NaN	NaN

	county	todays_date	hospitalized_covid_patients	all_hospital_beds	icu_available_beds
0	Plumas	2020-03-29	1.0	NaN	NaN
1	Tehama	2020-03-29	0.0	308.0	2.0
2	Glenn	2020-03-29	0.0	NaN	NaN
3	Mono	2020-03-29	1.0	308.0	2.0
4	Marin	2020-03-29	20.0	493.0	11.0

	county	date	7dayrollingavg_newlyconfirmed	7dayrollingavg_newdeath	todays_date	hospitalized_covid_patients	all_hospital_beds	icu_available_beds
0	Alameda	2020-03-29	1.153915	0.042738	2020-03-29	127.0	2725.0	119.0
1	Alameda	2020-03-30	1.384698	0.051285	2020-03-30	148.0	3283.0	146.0
2	Alameda	2020-03-31	1.478721	0.042738	2020-03-31	150.0	1857.0	77.0
3	Alameda	2020-04-01	1.307770	0.042738	2020-04-01	128.0	2456.0	106.0
4	Alameda	2020-04-02	1.478721	0.051285	2020-04-02	133.0	2580.0	112.0

ARIMA modeling¶

Pilot modeling using Los Angeles¶

Modeling for 7 Counties Highest New Cases/100k¶

Tehama County¶

Riverside County¶

Colusa County¶

Merced County¶

	7dayrollingavg_newlyconfirmed	7dayrollingavg_newdeath	hospitalized_covid_patients	all_hospital_beds	icu_available_beds
date
2020-03-29	2.468916	0.048382	1621.0	7394.0	345.0
2020-03-30	2.957007	0.056920	1878.0	9688.0	456.0
2020-03-31	3.369679	0.059766	1952.0	9460.0	445.0
2020-04-01	3.951689	0.079688	2071.0	10431.0	492.0
2020-04-02	4.219214	0.086803	2088.0	10349.0	488.0
...	...	...	...	...	...
2021-01-20	110.458032	2.033476	7413.0	19392.0	236.0
2021-01-21	97.337912	2.002170	7226.0	19411.0	274.0
2021-01-22	88.508157	1.999324	7014.0	19377.0	281.0
2021-01-23	82.928548	2.022092	6793.0	19334.0	299.0
2021-01-24	79.302728	2.009285	6606.0	19292.0	295.0

	hospitalized_covid_patients	7dayrollingavg_newdeath	all_hospital_beds	icu_available_beds
date
2021-01-02	7971.0	1.770220	20244.0	327.0
2021-01-03	8203.0	1.837101	20328.0	326.0
2021-01-04	8318.0	1.842793	20371.0	338.0
2021-01-05	8422.0	1.834255	20495.0	313.0
2021-01-06	8385.0	1.810064	20435.0	328.0

Dep. Variable:	y	No. Observations:	92
Model:	SARIMAX(0, 2, 2)	Log Likelihood	-283.304
Date:	Thu, 28 Jan 2021	AIC	572.608
Time:	20:53:22	BIC	580.108
Sample:	0	HQIC	575.632
	- 92
Covariance Type:	opg

	coef	std err	z	P>\|z\|	[0.025	0.975]
ma.L1	-1.1881	0.052	-22.820	0.000	-1.290	-1.086
ma.L2	0.2782	0.064	4.330	0.000	0.152	0.404
sigma2	30.9944	2.022	15.330	0.000	27.032	34.957

Ljung-Box (L1) (Q):	0.00	Jarque-Bera (JB):	298.43
Prob(Q):	0.97	Prob(JB):	0.00
Heteroskedasticity (H):	34.47	Skew:	0.23
Prob(H) (two-sided):	0.00	Kurtosis:	11.91

Ljung-Box (L1) (Q):	0.01	Jarque-Bera (JB):	353.54
Prob(Q):	0.93	Prob(JB):	0.00
Heteroskedasticity (H):	40.14	Skew:	0.76
Prob(H) (two-sided):	0.00	Kurtosis:	11.73

	coef	std err	z	P>\|z\|	[0.025	0.975]
ma.L1	-1.0460	0.050	-21.016	0.000	-1.144	-0.948
ma.L2	0.1922	0.065	2.938	0.003	0.064	0.320
sigma2	30.5668	1.908	16.017	0.000	26.827	34.307