What challenges are typically faced when using traditional machine learning algorithms for time series forecasting, and how do modern techniques address them?
- Challenges: Lack of capturing complex patterns, limited feature engineering. Modern techniques employ deep learning models, recurrent neural networks (RNNs), and attention mechanisms to better capture patterns and require less manual feature engineering.
- Challenges: Modern techniques use the same principles as traditional algorithms but with faster computation.
- Challenges: Traditional algorithms are perfect for time series forecasting.
- Challenges: Modern techniques use ensemble learning.
Traditional algorithms often struggle to capture complex patterns in time series data and require extensive feature engineering. Modern techniques leverage deep learning, RNNs, and attention mechanisms to automatically capture complex patterns, reducing the need for manual feature engineering.
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