Time series forecasting occurs when you make scientific predictions based on historical time stamped data . It involves building models through historical analysis and using them to make observations and drive future strategic decision-making.
Read moreWhich type of neural networks can be used for time series data?
Convolutional Neural Networks or CNNs are a type of neural network that was designed to efficiently handle image data. The ability of CNNs to learn and automatically extract features from raw input data can be applied to time series forecasting problems.
Read moreCan Ann be used for time series?
Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy .
Read moreWhat is the best neural network for time series prediction?
Conclusions. Recurrent Neural Networks are the most popular Deep Learning technique for Time Series Forecasting since they allow to make reliable predictions on time series in many different problems. The main problem with RNNs is that they suffer from the vanishing gradient problem when applied to long sequences.
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