text: Title: [BUG] Time series tabular model always uses fallback method (SeasonalNaive) for time series of length 1
  Body: **Bug Report Checklist**
  <!-- Please ensure at least one of the following to help the developers troubleshoot the problem:  -->
  - [x] I provided code that demonstrates a minimal reproducible example. <!-- Ideal, especially via source install --> 
  - [x] I confirmed bug exists on the latest mainline of AutoGluon via source install. <!-- Preferred --> 
  - [x] I confirmed bug exists on the latest stable version of AutoGluon. <!-- Unnecessary if prior items are checked --> 
  
  **Describe the bug**
  The tabular time series models identify time series of length 1 as too short for inference, even when differencing is set to 0. These time series are thus predicted using the fallback method, SeasonalNaive. Specifically, [this line of code](https://github.com/autogluon/autogluon/blob/ea2e8ff7082454565fbae31ebd5653851d5b4601/timeseries/src/autogluon/timeseries/models/autogluon_tabular/mlforecast.py#L368) is causing the issue.
  
  **Expected behavior**
  When differencing isn't applied, I'd expect the tabular time series models to produce predictions for time series of length 1, rather than using a fallback method.
  
  **To Reproduce**
  Following simplified example reproduces the issue. Two time series (item_ids ['4123__23', '7510__21']) are of length 1 and are predicted using the fallback method (SeasonalNaive), although sufficient data is available for a tabular prediction.
  ```python
  import pandas
  from autogluon.timeseries import TimeSeriesPredictor
  from pandas import Timestamp
  
  data = [
      {'item_id': '2527__18', 'timestamp': Timestamp('2008-01-01 00:00:00'), 'quantity_sold': 18.0},
      {'item_id': '2527__18', 'timestamp': Timestamp('2009-01-01 00:00:00'), 'quantity_sold': 682.0},
      {'item_id': '2572__16', 'timestamp': Timestamp('2006-01-01 00:00:00'), 'quantity_sold': 6.0},
      {'item_id': '2572__16', 'timestamp': Timestamp('2007-01-01 00:00:00'), 'quantity_sold': 18.0},
      {'item_id': '2572__16', 'timestamp': Timestamp('2008-01-01 00:00:00'), 'quantity_sold': 22.0},
      {'item_id': '2572__16', 'timestamp': Timestamp('2009-01-01 00:00:00'), 'quantity_sold': 74.0},
      {'item_id': '4123__23', 'timestamp': Timestamp('2009-01-01 00:00:00'), 'quantity_sold': 138.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2001-01-01 00:00:00'), 'quantity_sold': 4.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2002-01-01 00:00:00'), 'quantity_sold': 92.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2003-01-01 00:00:00'), 'quantity_sold': 40.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2004-01-01 00:00:00'), 'quantity_sold': 116.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2005-01-01 00:00:00'), 'quantity_sold': 48.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2006-01-01 00:00:00'), 'quantity_sold': 132.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2007-01-01 00:00:00'), 'quantity_sold': 6.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2008-01-01 00:00:00'), 'quantity_sold': 26.0},
      {'item_id': '695__24', 'timestamp': Timestamp('2009-01-01 00:00:00'), 'quantity_sold': 6.0},
      {'item_id': '7510__21', 'timestamp': Timestamp('2009-01-01 00:00:00'), 'quantity_sold': 56.0}
  ]
  
  df = pandas.DataFrame.from_dict(data)
  
  predictor = TimeSeriesPredictor(
      target='quantity_sold',
      prediction_length=2,
      freq='YS'
  )
  
  predictor.fit(
      train_data=df,
      hyperparameters={
          'DirectTabular': {},
      }
  )
  
  predictor.predict(df)
  ```
  
  **Screenshots / Logs**
  <!-- If applicable, add screenshots or logs to help explain your problem. -->
  ![image](https://github.com/user-attachments/assets/dd1cdd09-ee1d-4736-b7d1-80789a1a9d43)
  
  **Installed Versions**
  <!-- Please run the following code snippet: -->
  <details>
  
  ```python
  INSTALLED VERSIONS
  ------------------
  date                   : 2024-08-07
  time                   : 09:35:12.422961
  python                 : 3.11.9.final.0
  OS                     : Darwin
  OS-release             : 23.6.0
  Version                : Darwin Kernel Version 23.6.0: Fri Jul  5 17:53:24 PDT 2024; root:xnu-10063.141.1~2/RELEASE_ARM64_T6020
  machine                : arm64
  processor              : arm
  num_cores              : 12
  cpu_ram_mb             : 32768.0
  cuda version           : None
  num_gpus               : 0
  gpu_ram_mb             : []
  avail_disk_size_mb     : 871868
  accelerate             : 0.21.0
  autogluon              : 1.1.1
  autogluon.common       : 1.1.1
  autogluon.core         : 1.1.1
  autogluon.features     : 1.1.1
  autogluon.multimodal   : 1.1.1
  autogluon.tabular      : 1.1.1
  autogluon.timeseries   : 1.1.1
  boto3                  : 1.34.154
  catboost               : None
  defusedxml             : 0.7.1
  evaluate               : 0.4.2
  fastai                 : 2.7.16
  gluonts                : 0.15.1
  hyperopt               : 0.2.7
  imodels                : None
  jinja2                 : 3.1.4
  joblib                 : 1.4.2
  jsonschema             : 4.21.1
  lightgbm               : 4.3.0
  lightning              : 2.3.3
  matplotlib             : 3.9.1
  mlforecast             : 0.10.0
  networkx               : 3.3
  nlpaug                 : 1.1.11
  nltk                   : 3.8.1
  nptyping               : 2.4.1
  numpy                  : 1.26.4
  nvidia-ml-py3          : 7.352.0
  omegaconf              : 2.2.3
  onnxruntime-gpu        : None
  openmim                : 0.3.9
  optimum                : 1.17.1
  optimum-intel          : None
  orjson                 : 3.10.6
  pandas                 : 2.2.2
  pdf2image              : 1.17.0
  Pillow                 : 10.4.0
  psutil                 : 5.9.8
  pytesseract            : 0.3.10
  pytorch-lightning      : 2.3.3
  pytorch-metric-learning: 2.3.0
  ray                    : 2.10.0
  requests               : 2.32.3
  scikit-image           : 0.20.0
  scikit-learn           : 1.4.0
  scikit-learn-intelex   : None
  scipy                  : 1.12.0
  seqeval                : 1.2.2
  setuptools             : 72.1.0
  skl2onnx               : None
  statsforecast          : 1.4.0
  tabpfn                 : None
  tensorboard            : 2.17.0
  text-unidecode         : 1.3
  timm                   : 0.9.16
  torch                  : 2.3.1
  torchmetrics           : 1.2.1
  torchvision            : 0.18.1
  tqdm                   : 4.66.5
  transformers           : 4.40.2
  utilsforecast          : 0.0.10
  vowpalwabbit           : None
  xgboost                : 2.0.3
  ```
  
  </details>
  