Environment information:
- OS: [Windows 64]
- Python version: [3.6.5]
- TabPy release: [ 0.4.1]
Describe the issue
I am trying to run the forecasting code below in Tablue. I am getting an error saying
2021-01-31,18:48:06 [ERROR] (base_handler.py:base_handler:115): Responding with status=500, message="Error processing script", info="LinAlgError : Singular matrix"
2021-01-31,18:48:06 [ERROR] (web.py:web:2161): 500 POST /evaluate (::1) 2030.10ms
SCRIPT_REAL(
'
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
dates1 = _arg1
passeng = _arg2
order_arima = min(_arg3)
seasonal_diff = min (_arg4)
ma_param = min (_arg5)
months_forecast = min(_arg6)
ts = pd.DataFrame({"dates2": dates1,"Passengers": passeng})
ts["Passengers"] = ts["Passengers"].astype("float64")
ts = ts.set_index(["dates2"])
ts_log = np.log(ts)
ts_log.index = pd.to_datetime(ts_log.index)
ts_log_diff = ts_log - ts_log.shift()
ts_log_diff["Passengers"][0] = 0
from statsmodels.tsa.arima_model import ARIMA
model = ARIMA(ts_log_diff, order=(order_arima, seasonal_diff, ma_param))
results_ARIMA = model.fit(disp=-1)
predictions_value = results_ARIMA.forecast(months_forecast)[0]
from dateutil.relativedelta import relativedelta
add_month = relativedelta(months=1)
predictions_dates = list()
for i in range(months_forecast):
predictions_dates.append ( results_ARIMA.fittedvalues.index[-1] + ((i+1)*add_month))
forecast_log_diff = pd.Series(predictions_value, index=predictions_dates)
predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)
predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()
predictions_ARIMA_log = pd.Series(np.asscalar(ts_log.ix[0]), index=ts_log.index)
predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,fill_value=0)
predictions_ARIMA = np.exp(predictions_ARIMA_log)
forecast_log_diff_ARIMA = pd.Series(forecast_log_diff, copy=True)
forecast_ARIMA_log_diff_cumsum = forecast_log_diff_ARIMA.cumsum()
forecast_ARIMA_log = pd.Series(np.asscalar(ts_log.ix[-1]), index=forecast_log_diff_ARIMA.index)
forecast_ARIMA_log = forecast_ARIMA_log.add(forecast_ARIMA_log_diff_cumsum,fill_value=0)
forecast_ARIMA = np.exp(forecast_ARIMA_log)
forecast_ARIMA_2 = predictions_ARIMA.append(forecast_ARIMA)
forecast_ARIMA_2 = forecast_ARIMA_2[len(forecast_ARIMA):]
return list(forecast_ARIMA_2)
',
ATTR([Month]), ATTR([#Passengers]), min([AR (Time lag)]), MIN([I Seasonal Difference ]),
MIN([MA (Moving average)]), MIN([Months Forecast]))
To Reproduce
https://www.clearpeaks.com/tableau-python/
Expected behavior
That the forecast works plots a line.
Environment information:
Describe the issue
I am trying to run the forecasting code below in Tablue. I am getting an error saying
2021-01-31,18:48:06 [ERROR] (base_handler.py:base_handler:115): Responding with status=500, message="Error processing script", info="LinAlgError : Singular matrix"
2021-01-31,18:48:06 [ERROR] (web.py:web:2161): 500 POST /evaluate (::1) 2030.10ms
SCRIPT_REAL(
'
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
dates1 = _arg1
passeng = _arg2
order_arima = min(_arg3)
seasonal_diff = min (_arg4)
ma_param = min (_arg5)
months_forecast = min(_arg6)
ts = pd.DataFrame({"dates2": dates1,"Passengers": passeng})
ts["Passengers"] = ts["Passengers"].astype("float64")
ts = ts.set_index(["dates2"])
ts_log = np.log(ts)
ts_log.index = pd.to_datetime(ts_log.index)
ts_log_diff = ts_log - ts_log.shift()
ts_log_diff["Passengers"][0] = 0
from statsmodels.tsa.arima_model import ARIMA
model = ARIMA(ts_log_diff, order=(order_arima, seasonal_diff, ma_param))
results_ARIMA = model.fit(disp=-1)
predictions_value = results_ARIMA.forecast(months_forecast)[0]
from dateutil.relativedelta import relativedelta
add_month = relativedelta(months=1)
predictions_dates = list()
for i in range(months_forecast):
predictions_dates.append ( results_ARIMA.fittedvalues.index[-1] + ((i+1)*add_month))
forecast_log_diff = pd.Series(predictions_value, index=predictions_dates)
predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)
predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()
predictions_ARIMA_log = pd.Series(np.asscalar(ts_log.ix[0]), index=ts_log.index)
predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,fill_value=0)
predictions_ARIMA = np.exp(predictions_ARIMA_log)
forecast_log_diff_ARIMA = pd.Series(forecast_log_diff, copy=True)
forecast_ARIMA_log_diff_cumsum = forecast_log_diff_ARIMA.cumsum()
forecast_ARIMA_log = pd.Series(np.asscalar(ts_log.ix[-1]), index=forecast_log_diff_ARIMA.index)
forecast_ARIMA_log = forecast_ARIMA_log.add(forecast_ARIMA_log_diff_cumsum,fill_value=0)
forecast_ARIMA = np.exp(forecast_ARIMA_log)
forecast_ARIMA_2 = predictions_ARIMA.append(forecast_ARIMA)
forecast_ARIMA_2 = forecast_ARIMA_2[len(forecast_ARIMA):]
return list(forecast_ARIMA_2)
',
ATTR([Month]), ATTR([#Passengers]), min([AR (Time lag)]), MIN([I Seasonal Difference ]),
MIN([MA (Moving average)]), MIN([Months Forecast]))
To Reproduce
https://www.clearpeaks.com/tableau-python/
Expected behavior
That the forecast works plots a line.