Deep Sequence Models for Time Series Data: A Comparative Study and Parameter Fine-Tuning Approach

The modeling and prediction of time series data have become pivotal in data science and machine learning. Deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, have proven effective in capturing complex temporal relationships, outperforming traditional methods. This study compares the performance of three LSTM variants-Convolutional LSTM (ConvLSTM), Bidirectional LSTM (BiLSTM), and Stacked LSTM-using datasets such as ETTh, ETTm, Energy, AQI, and Exchange Rate. Each model is trained and tested on subsets of these datasets, with hyperparameters like the number of layers, neurons, learning rate, batch size, optimizer, and loss function fine-tuned for optimal performance. The models are evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results indicate that ConvLSTM excels with spatiotemporal datasets, BiLSTM effectively captures full sequence contexts, and Stacked LSTM benefits from more in-depth representation. This research enhances the understanding of LSTM variants in time series analysis, demonstrating that fine-tuning can significantly boost performance. The findings apply to improving accuracy in time series forecasting across various fields.