![]() We will now discuss the methodology and a simple test sequence on which we will apply our model. Hence, if we are able to convert the 1D time-series sequence to an input image matrix shape, we could apply a CNN model for the forecasting problem. Source link : Python For Finance Cookbook Therefore, for a CNN model the above image is interpreted (in an over-simplified example) as a matrix as shown below : This 3D matrix then goes in as an input to the CNN model. Overall, we have a 3D matrix of dimension. Hence, the image would consist of 3 matrices of the size of dimensions. Also, since it is a color image, it would have 3 channels. A convolutional neural network perceives each image as a matrix of pixel values in the dimension of image width, length, and the number of channels.įor example, let us assume that the puppy’s image is 2000 pixels wide and 2500 pixels in height. A color image has three channels comprising red, blue, and green colors. A grey image has one channel since each channel corresponds to the colors it contains. Each image also comprises channels depending on the color composition of the image. ![]() Each pixel value can range from 0 to 255 depending on the intensity of the pixel. ![]() Every image that is digitally available is actually a matrix of pixel values. ![]()
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