Closed plaban1981 closed 2 years ago
Sorry this issue still persists. Please consider it open
Machine_learning_Assignment (1).zip Attached is the sample notebook
Hi @plaban1981 👍 Thanks for the detailed notebook. yes I found the bug and fixed it, you should upgrade your auto-ts version like this:
pip install auto-ts --upgrade
and run it. All the best! AutoViML team
Thanks a lot for the prompt resolution. It surely does executes for Prophet now . Much Appreciated. Machine_learning_Assignment__using_Auto_Timeseries.zip
But when I try to apply ARIMA, ML models I encounter the below error
UnboundLocalError Traceback (most recent call last)
Hi @plaban1981 👍
I found that you should set seasonality = False and run ARIMA first. Then try it setting as True.
I found the bug and fixed it, you should upgrade your auto-ts version like this:
pip install auto-ts --upgrade
and run it. All the best! AutoViML team
<class 'pandas.core.frame.DataFrame'> Int64Index: 101952 entries, 0 to 103391 Data columns (total 8 columns):
Column Non-Null Count Dtype
0 datetime 101952 non-null datetime64[ns] 1 load 101952 non-null float64
2 apparent_temperature 101952 non-null float64
3 temperature 101952 non-null float64
4 humidity 101952 non-null float64
5 dew_point 101952 non-null float64
6 wind_speed 101952 non-null float64
7 cloud_cover 101952 non-null float64
dtypes: datetime64ns, float64(7) memory usage: 7.0 MB
sample data
datetime load apparent_temperature temperature humidity dew_point wind_speed cloud_cover 0 2018-01-01 00:00:00 803.22270 10.45800 10.45800 0.955500 8.946000 0.0 0.0 1 2018-01-01 00:15:00 774.89523 10.32675 10.32675 0.961625 8.911875 0.0 0.0 2 2018-01-01 00:30:00 731.46927 10.19550 10.19550 0.967750 8.877750 0.0 0.0 3 2018-01-01 00:45:00 713.93870 10.06425 10.06425 0.973875 8.843625 0.0 0.0 4 2018-01-01 01:00:00 699.23007 9.93300 9.93300 0.980000 8.809500 0.0 0.0
model = auto_timeseries( score_type='rmse', time_interval='T,min', non_seasonal_pdq=None, seasonality=True, seasonal_period=60, model_type=['Prophet'], verbose=2)
model.fit( traindata=train, ts_column="datetime", target="load", cv=5, sep="," )
While training I emncounter the below error : Start of Fit..... Running Augmented Dickey-Fuller test with paramters: maxlag: 31 regression: c autolag: BIC Results of Augmented Dickey-Fuller Test: +-----------------------------+------------------------------+ | | Dickey-Fuller Augmented Test | +-----------------------------+------------------------------+ | Test Statistic | -15.883436634196332 | | p-value | 8.704767665249516e-29 | | #Lags Used | 27.0 | | Number of Observations Used | 101924.0 | | Critical Value (1%) | -3.430414160204652 | | Critical Value (5%) | -2.8615683578111595 | | Critical Value (10%) | -2.5667850938695476 | +-----------------------------+------------------------------+ this series is stationary Target variable given as = load Start of loading of data..... Input is data frame. Performing Time Series Analysis ts_column: datetime sep: , target: load Using given input: pandas dataframe... datetime column exists in given train data... Type of time series column datetime is float or unknown. Must be string or datetime. Please check input and try again.
TypeError Traceback (most recent call last)