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Hi,
I'm trying to run a python script that will essentially perform time series forecasting on my input data.
What I want is the next 12 predicted values.
I did this in jupyter but not able to do it in power bi as I'm getting the same dataset instead of predictions in power bi
What is wrong?
Dataset has 110 records. 2 records pasted below:
date | tcv |
1/1/2013 | 2011131 |
2/1/2013 | 2053142 |
My code:
import numpy as np
import matplotlib.pyplot as plt
import pypyodbc as pyodbc
import pandas as pd
from statsmodels.tsa.arima_model import ARMA,ARMAResults,ARIMA,ARIMAResults
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf # for determining (p,q) orders
from statsmodels.tsa.holtwinters import ExponentialSmoothing
SERVER_NAME = 'servername.its.corp.net'
DATABASE_NAME = 'GDB'
cnxn = pyodbc.connect(Driver='{SQL Server}', Server=SERVER_NAME , database=DATABASE_NAME,
trusted_connection='Yes')
cursor = cnxn.cursor()
sql_query = """
select * from my_table
"""
# With Headers
df = pd.read_sql(sql_query, cnxn,index_col='date',parse_dates=True)
#index col is required to make sure stasmodel on this dataset we need to set index frequency
df.index.freq = 'MS'
method_TESmul12 = ExponentialSmoothing(df['tcv'],trend='mul',seasonal='mul',seasonal_periods=12).fit()
dataset = method_TESmul12.forecast(12)
Hi @klehar ,
I have tested with Python 3.10.2 and the results in Power BI and Python are the same.
Here is the table I used to test.
date | tcv |
2013-01-01 | 2011131 |
2013-02-01 | 2053142 |
2013-03-01 | 2033123 |
2013-04-01 | 2040222 |
2013-05-01 | 2044113 |
2013-06-01 | 2056333 |
2013-07-01 | 2060245 |
2013-08-01 | 2058756 |
2013-09-01 | 2068524 |
2013-10-01 | 2070555 |
2013-11-01 | 2056700 |
2013-12-01 | 2069322 |
2014-01-01 | 2080246 |
2014-02-01 | 2076623 |
2014-03-01 | 2075456 |
2014-04-01 | 2068456 |
2014-05-01 | 2078123 |
2014-06-01 | 2074235 |
2014-07-01 | 2080654 |
2014-08-01 | 2081456 |
2014-09-01 | 2090231 |
2014-10-01 | 2085165 |
2014-11-01 | 2064897 |
2014-12-01 | 2070542 |
2015-01-01 | 2065894 |
2015-02-01 | 2075698 |
Best Regards,
Icey
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
So I made some modifications to the code and Im getting the output now.
However, my power bi and python show different predictions
Wonder where I have to set the seed, if that is the case
NewCode
import numpy as np
import matplotlib.pyplot as plt
import pypyodbc as pyodbc
import pandas as pd
from statsmodels.tsa.arima_model import ARMA,ARMAResults,ARIMA,ARIMAResults
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf # for determining (p,q) orders
from statsmodels.tsa.holtwinters import ExponentialSmoothing
SERVER_NAME = 'db.its.corp.net'
DATABASE_NAME = 'GDB'
cnxn = pyodbc.connect(Driver='{SQL Server}', Server=SERVER_NAME , database=DATABASE_NAME,
trusted_connection='Yes')
cursor = cnxn.cursor()
sql_query = """
select * from my_table
"""
# With Headers
df = pd.read_sql(sql_query, cnxn,index_col='date',parse_dates=True)
#index col is required to make sure stasmodel on this dataset we need to set index frequency
df.index.freq = 'MS'
method_TESmul12 = ExponentialSmoothing(df['tcv'],trend='mul',seasonal='mul',seasonal_periods=12).fit()
range = pd.date_range('01-02-2022',periods=12,freq='MS')
predictions = method_TESmul12.forecast(12).astype(int)
predictions_range = pd.DataFrame({'Date':range, 'TCV':predictions})
predictions_range
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