MLP time series

We will be following the below-mentioned pathway for applying CNNs to a univariate 1D time series :

Research has shown that using CNNs for time series classification has several important advantages over other methods. They are highly noise-resistant models, and they are able to extract very informative, deep features, which are independent from time .4 Eki 2019

CNNs are commonly used in solving problems related to spatial data, such as images. RNNs are better suited to analyzing temporal, sequential data, such as text or videos . A CNN has a different architecture from an RNN.

CNN, although popular in image datasets, can also be used (and may be more practical than RNNs) on time series data. Present a popular architecture for time series classification (univariate AND multivariate) called Fully Convolutional Neural Network (FCN)21 Eki 2020