CNN-LSTM architecture for time series forecasting

Deep learning has offered compelling solutions for time series forecasting, and one of the notable architectures in this space is the CNN-LSTM model. This model leverages the strengths of CNNs and LSTM networks, providing an effective framework for handling the unique characteristics of time series data. CNNs are renowned for their performance in image processing tasks due to their ability to learn spatial patterns in images, while in sequential data, they can learn local patterns. The convolutional layers within the network apply a series of filters to the data, learning and extracting significant local and global temporal patterns and trends. These features act as a compressed representation of the original data, retaining essential information while reducing dimensionality. The reduction in dimensionality leads to a more efficient representation that captures relevant patterns.

Once significant features have been extracted through...