RNNs in time series forecasting
Time series forecasting poses a unique challenge in the world of machine learning, involving the prediction of future values based on previously observed sequential data. An intuitive way of thinking about this is to consider a sequence of past data points. The question then becomes, given this sequence, how can we predict the next data point or sequence of data points? This is where RNNs demonstrate their efficacy. RNNs are a specific type of neural network developed to process sequential data. They maintain an internal state or “memory” that holds information about the elements of the sequence observed thus far. This internal state is updated at each step of the sequence, amalgamating information from the new input and the previous state. As an example, while predicting sales, an RNN may retain data regarding the sales trends from the previous months, the overall trend across the past year, and the seasonality effects, among others.
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