from tensorflow.keras import layers from sklearn import neighbors from sklearn.model_selection import GridSearchCV from pandas.util.testing import assert_frame_equal goog = Stocker('GOOGL') goog.plot_stock() # Create model model, model_data = goog.create_prophet_model(days=90) goog.evaluate_prediction() # Optimize the model goog.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2]) goog.changepoint_prior_validation(start_date='2016-01-04', end_date='2017-01-03', changepoint_priors=[0.001, 0.05, 0.1, 0.2]) # Evaluate the new model goog.evaluate_prediction() print(goog.evaluate_prediction(nshares=1000)) # Getting the dataframe of the data goog_data = goog.make_df('2004-08-19', '2018-03-27') print(goog_data.head(50)) goog_data = goog_data[[ 'Date', 'Open', 'High', 'Low', 'Close', 'Adj. Close', 'Volume' ]] print(goog_data.head(50))
amazon.weekly_seasonality = True model, model_data = amazon.create_prophet_model() model.plot_components(model_data) plt.show() amazon.weekly_seasonality = False model, model_data = amazon.create_prophet_model(days=90) amazon.evaluate_prediction() amazon.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2]) amazon.changepoint_prior_validation(start_date='2016-01-04', end_date='2017-01-03', changepoint_priors=[0.001, 0.05, 0.1, 0.2]) amazon.changepoint_prior_validation( start_date='2016-01-04', end_date='2017-01-03', changepoint_priors=[0.15, 0.2, 0.25, 0.4, 0.5, 0.6]) amazon.changepoint_prior_scale = 0.5 amazon.evaluate_prediction() amazon.weekly_seasonality = True amazon.evaluate_prediction()