CNNs for time series forecasting
CNNs have recorded remarkable success in image classification tasks due to their ability to detect localized patterns within grid-like data structures. This idea can also be applied to time series forecasting. By viewing a time series as a sequence of temporal intervals, CNNs can extract and recognize patterns that are predictive of future trends. Another important strength of CNNs is their translation-invariant nature. This means once they learn a pattern in one segment, the network is well equipped to recognize it everywhere else it occurs within the series. This comes in handy in detecting reoccurring patterns across time steps.
The setup of a CNN also helps to automatically reduce the dimensionality of our input data with the aid of the pooling layers. Hence, the convolution and pooling operations in a CNN transform the input series into a streamlined form that captures the core features while ensuring computational efficiency. Unlike with images...