silver-loadings-forecast-1.py

## Read in data (skip 2028 Silver Institute 'predicted' entry)
est_thrifting = pd.read_excel('../data/silver_solar_thrifting_estimates.xlsx').iloc[0:10]
fc_loadings = pd.DataFrame(columns=['ds','y'])
fc_loadings['ds'] = est_thrifting['Year'].values
fc_loadings['ds']  = fc_loadings['ds'].apply(lambda d: datetime.datetime.strptime(str(d), "%Y")) 
fc_loadings['y'] = est_thrifting['Market Average Silver Loadings (g/cell)'].values
model_loadings = fbprophet.Prophet(changepoint_prior_scale=5)
model_loadings.fit(fc_loadings)
future_df_loadings = model_loadings.make_future_dataframe(periods=30, freq='y')
forecast_loadings = model_loadings.predict(future_df_loadings)
model_loadings.plot(forecast_loadings);