<jupyter_start><jupyter_text>Prophet<jupyter_code># setting model parameters to seasonality_mode='multiplicative'

m = Prophet(seasonality_mode='multiplicative')
m.fit(Prophetdf)

# predicting part

future = m.make_future_dataframe(200, freq='MS')
forecast = m.predict(future)
fig = m.plot(forecast)<jupyter_output>INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.<jupyter_text>Even with seasonality mode set to multiplicative, the model doesn't seem to acknowledge the increasing range of index numbers in the dataset. The ``plot_components`` function gives some background on the components of the forecast below:<jupyter_code>fig = m.plot_components(forecast)<jupyter_output><empty_output><jupyter_text>evidently, the trend confidence interval gets progressively worse with time. I did some soul searching on the prophet website and it's possible we need different args in the actual model. The code below I copied from an example on the [page](https://facebook.github.io/prophet/docs/multiplicative_seasonality.html) and it looks...interesting. I don't know what all the arguments mean, but the confidence interval is suspiciously good?<jupyter_code>m = Prophet(seasonality_mode='multiplicative')
m.add_seasonality('quarterly', period=91.25, fourier_order=8, mode='additive')
m.fit(Prophetdf)

future = m.make_future_dataframe(200, freq='MS')
forecast = m.predict(future)
fig = m.plot(forecast)

fig = m.plot_components(forecast)
from prophet.plot import add_changepoints_to_plot
fig = m.plot(forecast)
a = add_changepoints_to_plot(fig.gca(), m, forecast)<jupyter_output><empty_output><jupyter_text>Trying annual data instead<jupyter_code>Idf.head()

anndf = pd.DataFrame(Idf['ALLSKY_KT'])

anndf2 = anndf.loc[anndf.index[12::13]]
anndf2
#Prophetdf.index[12::13]
# renaming first column

model_ann = anndf2.rename(columns={"ALLSKY_KT": "y"})


def append(dfseries):
    dfseries = dfseries[:4] #+ '/' + dfseries[4:]
    return dfseries

model_ann['ds']=model_ann.index
model_ann['ds']= model_ann['ds'].astype(str)

model_ann['ds'] = model_ann['ds'].apply(append)
#a_string = a_string[:1] + "b" + a_string[1:]
#model_ann
plt.scatter(x = model_ann['ds'].values.astype(int), y=model_ann['y'].values)
z = Prophet()
z.fit(model_ann);

future = z.make_future_dataframe(20, freq='YS')
forecast = z.predict(future)
fig = z.plot(forecast)<jupyter_output>INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
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