To train a neural network on historical temperature data to predict future temperatures, what technique is used to turn the long list of temperatures into pairs of 'input history' and 'output future'?

The technique used to transform a long list of historical temperature data into pairs of 'input history' and 'output future' for training a neural network is called sliding window or windowing. This method is specifically designed for preparing sequential data, such as time series like temperature records, for supervised learning tasks where past observations are used to predict future values. To apply the sliding window technique, two essential parameters are defined: an input window size, also known as the look-back period, and an output window size, also known as the prediction horizon. These parameters determine the length of the sequences that will form the input and output parts of each training example. The process begins by....