diff options
Diffstat (limited to 'openbb_terminal/forecast/helpers.py')
| -rw-r--r-- | openbb_terminal/forecast/helpers.py | 15 |
1 files changed, 10 insertions, 5 deletions
diff --git a/openbb_terminal/forecast/helpers.py b/openbb_terminal/forecast/helpers.py index cfefcb8191f..95e5e698a31 100644 --- a/openbb_terminal/forecast/helpers.py +++ b/openbb_terminal/forecast/helpers.py @@ -986,8 +986,9 @@ def dt_format(x) -> str: Returns: x: formatted string """ - x = pd.to_datetime(x) - x = x.strftime("%Y-%m-%d") + # x = pd.to_datetime(x, infer_datetime_format=False) + + # x = x.strftime("%Y-%m-%d") if x.time() == datetime.time(0,0) else x.strftime("%Y-%m-%d %H:%M:%S") return x @@ -1002,9 +1003,10 @@ def get_series( df=data, time_col=time_col, value_cols=[target_column], - freq="B", + freq=None, fill_missing_dates=True, ) + freq = "D" try: # for the sdk, we must check if date is a column not an index # check if date is in the index, if true, reset the index @@ -1014,7 +1016,10 @@ def get_series( # reset the index data.reset_index(drop=True, inplace=True) # remove 00:00:00 from 2019-11-19 00:00:00 - data[time_col] = data[time_col].apply(lambda x: dt_format(x)) + # data[time_col] = data[time_col].apply(lambda x: dt_format(x)) + freq = pd.infer_freq(data[time_col]) + + filler_kwargs["freq"] = freq ticker_series = TimeSeries.from_dataframe(**filler_kwargs) except ValueError: @@ -1078,7 +1083,7 @@ def get_prediction( ): _, val = ticker_series.split_before(train_split) - console.print(f"Predicting {model_name} for {n_predict} days") + console.print(f"Predicting {model_name} for {n_predict} periods") if model_name not in ["Regression", "Logistic Regression"]: # need to create a new pytorch trainer for historical backtesting to remove progress bar best_model.trainer = None |