Time Series Analysis is a statistical method that involves analyzing and interpreting data points collected or recorded over a period of time. In a time series, each data point is associated with a specific timestamp or time interval, and the sequential order of observations is crucial. This type of analysis is employed in various fields to uncover patterns, trends, and underlying structures within the temporal data. Time series analysis is particularly valuable for forecasting future values based on historical patterns.
We experimentally demonstrated the real-time operation of a photonic neuron with a self-connection, a prerequisite for integrated recurrent neural networks (RNNs). After studying two applications, we propose a photonics-assisted platform for time series prediction and classification.
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