Overcoming the Challenges of Uncertainty in Forecasting Economic Time Series Through Convolutional Neural Networks and Other Intelligent Approaches

This article provides insights into the use of artificial neural networks (ANNs) and convolutional (CNNs) as tools for forecasting economic time series, where uncertainty refers to incomplete information about future. To improve ability CNN architectures capture long-term dependencies in input sequence we used WaveNet models which dilate convolutions with skip connections sequence. The residual blocks are defined a specific way that allows easier flow through network while avoiding vanishing gradient problem, making it potential innovation field deep learning. Another innovative aspect is one-hot encoding target sequences using categorical cross-entropy loss function.