Linear regression

Predict the time series data

Give a data frame with these columns





Base data can come from Yahoo driver


The model will predict SVR, LINEAR, ARIMA for 20 days on Close value

dataset ="TSLA")
pr = naas_drivers.prediction.get(dataset=dataset, prediction_type="all")

Prediction size

  • data_points: The number of days in the future that are to predict.

dataset ="TSLA")
pr = naas_drivers.prediction.get(dataset=dataset, data_points=50)


All the parameters of the above formula are explained below.

  • prediction_type: The model to predict, it can be SVR, LINEAR, ARIMA, or all

dataset ="TSLA")
pr = naas_drivers.prediction.get(dataset=dataset, prediction_type="ARIMA")


  • dataset : the dataset in DataFrame format

  • label: The exact name of the column that is to be predicted, from the dataset

  • date_column:The date range from the dataset. Will be used as the output index.

  • prediction_type: Can be ARIMA, LINEAR, SVR, COMPOUND or all

  • data_points (optional): number of days to predict

  • concact_label (optional): A column name who will generate a concatenated frame with past and future data.

dataset ="TSLA")
pr = naas_drivers.prediction.get(dataset=dataset)


Once you have predicted using the above predict formula, you can plot the predictions

naas_drivers.plotly.stock(pr, , "linechart_close")

Check more options on the link below