CPI Forecasting with ARIMA

How can i do ARIMA Forecast for economic variables like Swiss CPI, code below does not work, whick Field ID works?

spot_df = rd.get_history(
"CHCPIY=ECI", 'CTBTR_1', interval='monthly', start="2024-01-01", count=10000)

print(spot_df)
_e_date = datetime.fromtimestamp(datetime.timestamp(spot_df.index[-1]))
e_date = _e_date + timedelta(days=1)
e_date = e_date.strftime('%Y-%m-%d')
_e_date = _e_date + timedelta(days=30)
fce_date = _e_date.strftime('%Y-%m-%d')

decomposition = seasonal_decompose(spot_df.TRDPRC_1, period=1)
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid

spot_df['spot_1diff'] = spot_df['CTBTR_1'].diff()

spot_df = spot_df[spot_df['spot_1diff'].notnull()] # drop null rows

lag_acf = acf(spot_df['spot_1diff'], nlags=50)

lag_pacf = pacf(spot_df['spot_1diff'], nlags=50, method='ols')

new_spot = spot_df['spot_1diff'].resample('D').ffill() # resample per day and fill the gaps

new_spot = new_spot.bfill()

new_spot = new_spot.astype('float')

arma_model = ARIMA(new_spot, order=(1, 0, 1))

results = arma_model.fit()

residuals = pd.DataFrame(results.resid)

predictions_ARIMA = pd.Series(results.fittedvalues, copy=True)

predictions_ARIMA_cumsum = predictions_ARIMA.cumsum()

predictions_ARIMA_final = pd.Series(

spot_df['CTBTR_1'].iloc[0], index=new_spot.index)

predictions_ARIMA_final = predictions_ARIMA_final.add(

predictions_ARIMA_cumsum, fill_value=0)

new_spot = spot_df.CTBTR_1.resample(

'D', label='right').ffill().astype('float')

es_model = ExponentialSmoothing(

new_spot, trend='add', damped=False, seasonal='mul', seasonal_periods=30)

es_results = es_model.fit()

predicted_values = es_model.predict(

params=es_results.params, start=e_date, end=fce_date)

preds = pd.DataFrame(index=pd.date_range(

start=e_date, end=fce_date), data=predicted_values, columns=['CTBTR_1'])

plt.figure(figsize=(16, 7))

plt.plot(new_spot, label='Actual')

plt.plot(preds, label='Forecast', color='pink')

plt.legend(loc='best')

plt.show()