Open Alex-Mak-MCW opened 1 week ago
Things to try:
Recurrent Neural Networks (RNNs): Implement basic RNNs for capturing sequential dependencies in time series data.
Long Short-Term Memory (LSTM): Use LSTMs to model more complex dependencies and long-term sequences.
Gated Recurrent Unit (GRU): Apply GRUs as a simpler alternative to LSTMs.
Convolutional Neural Networks (CNNs): Combine CNNs with LSTMs for feature extraction from time series data.
Seq2Seq Models: Implement sequence-to-sequence models for time series forecasting.
ARIMA-LSTM Hybrid: Combine ARIMA for modeling linear components and LSTMs for non-linear patterns.
DeepAR: Use probabilistic forecasting with models like DeepAR (if working with multiple time series).
State Space Models: Apply deep learning-based state space models for multivariate time series.
Things to try:
Recurrent Neural Networks (RNNs): Implement basic RNNs for capturing sequential dependencies in time series data.
Long Short-Term Memory (LSTM): Use LSTMs to model more complex dependencies and long-term sequences.
Gated Recurrent Unit (GRU): Apply GRUs as a simpler alternative to LSTMs.
Convolutional Neural Networks (CNNs): Combine CNNs with LSTMs for feature extraction from time series data.
Seq2Seq Models: Implement sequence-to-sequence models for time series forecasting.
ARIMA-LSTM Hybrid: Combine ARIMA for modeling linear components and LSTMs for non-linear patterns.
DeepAR: Use probabilistic forecasting with models like DeepAR (if working with multiple time series).
State Space Models: Apply deep learning-based state space models for multivariate time series.