<jupyter_start><jupyter_text>Training on COVID Data Set Harrisburg University PhD Comprehensive Exam 2021This code implementation aims to predict COVID-19 outbreak trend and expansion rate across different countries. Using time series analysis with NeuralProphet Model and Libraries, this examine autocorrelations of overall trend (regardless of countries) and evaluate the model performance result.<jupyter_code>from google.colab import drive
drive.mount('/content/drive')
if 'google.colab' in str(get_ipython()):
    !pip install git+https://github.com/ourownstory/neural_prophet.git 
    
import pandas as pd
from neuralprophet import NeuralProphet, set_log_level
# set_log_level("ERROR")
# data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
data_location = '/content/drive/MyDrive/HU/ANLY 565/R script'


sf_load_df = pd.read_excel(data_location +  '/Copy of covid.xlsx')
sf_load_df.head(3)<jupyter_output><empty_output><jupyter_text>Generic forecast: Time-based features onlyIn this first section, we will train a model with time-features only like we would do with Facebook Prophet.<jupyter_code>m = NeuralProphet(
    weekly_seasonality=6,
    daily_seasonality=10,
    trend_reg=1,
    learning_rate=0.03,
)
df_train, df_test = m.split_df(sf_load_df, freq='H', valid_p = 1.0/12)

metrics = m.fit(df_train, freq='H', validation_df=df_test, plot_live_loss=True)
metrics.tail(1)
forecast = m.predict(df_train)
fig = m.plot(forecast)
forecast = m.predict(df_test)
m = m.highlight_nth_step_ahead_of_each_forecast(1)
fig = m.plot(forecast[-7*24:])
fig_param = m.plot_parameters()<jupyter_output><empty_output>
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