sarimax in automl_time_series_forecast.ipynb sometimes suggest wrong config

Hi,

I tried the [automl_time_series_forecast](https://github.com/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb) example, but it fails occasionally because of an invalid config suggested. Below is the code to reproduce it. I set the invalid config in my trials as the starting_points.

```python
import statsmodels.api as sm

data = sm.datasets.co2.load_pandas().data
# data is given in weeks, but the task is to predict monthly, so use monthly averages instead
data = data["co2"].resample("MS").mean()
data = data.bfill().ffill() # makes sure there are no missing values
data = data.to_frame().reset_index()

# split the data into a train dataframe and X_test and y_test dataframes, where the number of samples for test is equal to
# the number of periods the user wants to predict
num_samples = data.shape[0]
time_horizon = 12
split_idx = num_samples - time_horizon
train_df = data[
:split_idx
] # train_df is a dataframe with two columns: timestamp and label
X_test = data[split_idx:][
"index"
].to_frame() # X_test is a dataframe with dates for prediction
y_test = data[split_idx:][
"co2"
] # y_test is a series of the values corresponding to the dates for prediction

""" import AutoML class from flaml package """
from flaml import AutoML

automl = AutoML()

settings = {
"time_budget": 60, # total running time in seconds
"metric": "mape", # primary metric for validation: 'mape' is generally used for forecast tasks
"task": "ts_forecast", # task type
"log_file_name": "CO2_forecast.log", # flaml log file
"eval_method": "holdout", # validation method can be chosen from ['auto', 'holdout', 'cv']
"seed": 7654321, # random seed
"verbose": 5,
"estimator_list": ["sarimax"], # list of ML learners
"starting_points": {
"sarimax": {"p": 4, "d": 0, "q": 5, "P": 1, "D": 3, "Q": 2, "s": 4}
},
}

"""The main flaml automl API"""
automl.fit(
dataframe=train_df, # training data
label="co2", # label column
period=time_horizon, # key word argument 'period' must be included for forecast task)
**settings
)
```

It will raise ValueError:
`ValueError: Invalid model: autoregressive lag(s) {4} are in both the seasonal and non-seasonal autoregressive components.`

Would it be possible that we ensure the suggested configs are always valid? Thanks.