text: Title: [BUG] Time Series Predictor Model Fit Causing Kernel to Die (Jupyter Notebook)
  Body: **Bug Report Checklist**
  <!-- Please ensure at least one of the following to help the developers troubleshoot the problem:  -->
  - [ ] I provided code that demonstrates a minimal reproducible example. <!-- Ideal, especially via source install --> 
  - [ ] I confirmed bug exists on the latest mainline of AutoGluon via source install. <!-- Preferred --> 
  - [ ] I confirmed bug exists on the latest stable version of AutoGluon. <!-- Unnecessary if prior items are checked --> 
  
  **Describe the bug**
  <!-- A clear and concise description of what the bug is. -->
  
  I am running a Jupyter Notebook on an M2 Macbook Pro with 24 GB of RAM where I am using Autogluon to run a Time Series Predictor model. I am having issues when fitting the model, due to my kernel dying.  I have tried 3 things:
  
  1. Uninstalling Autogluon and reinstalling it using the provided method on the Autogluon website.
  2. Decreasing the size of the data set 
  3. Excluding certain hyperparameters in the fit
  
  With doing these my kernel still continues to crash, but when running on colleagues computer it runs with no issues. I am trying to understand why this is an issue on my computer. 
  
  **Expected behavior**
  <!-- A clear and concise description of what you expected to happen. -->
  
  What is expected when fitting the Autogluon Time Series Predictor Model is a verbose output showing the system information about Autogluon, Python and your computer. Followed by information of the model you are trying to fit, then the information of the fit (Validation score, runtime information).
  
  
  **Installed Versions**
  <!-- Please run the following code snippet: -->
  <details>
  
  Here are my systems info for running the following code:
  
  AutoGluon Version:  1.1.1
  Python Version:     3.9.16
  Operating System:   Darwin
  Platform Machine:   arm64
  CPU Count:          8
  GPU Count:          0
  Memory Avail:       12.45 GB / 24.00 GB (51.9%)
  Disk Space Avail:   645.40 GB / 926.35 GB (69.7%)
  
  Below is the Code that I am trying to run:
  
  ```python
  # Fit the predictor with the specified models
  predictor.fit(
      training_data,
      hyperparameters={
          'SeasonalNaive': {},              # Seasonal Naive
          'CrostonSBA': {},                 # Croston's method with SBA
          'NPTS': {},                       # Non-Parametric Time Series
          'AutoETS': {},                    # Auto Exponential Smoothing
          'DynamicOptimizedTheta': {},      # Dynamic Optimized Theta
          'AutoARIMA': {},                  # Auto ARIMA
          'RecursiveTabular': {},           # Recursive Tabular Model
          'DirectTabular': {},              # Direct Tabular Model
          'DeepAR': {},                     # DeepAR
          'TemporalFusionTransformer': {}, # Temporal Fusion Transformer
          'PatchTST': {}                   # Patch Transformer           
      },
      num_val_windows=1,
      time_limit=3600
  )
  ```
  
  Note: With the Data Set not being too large the notebook suggested to decrease the num_val_windows from 5 to 1, but still no improvement. Also the same with decreasing the time_limit.
  
  </details>
  