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INTERACTIVE- Live Tracker -Russia-Ukraine war by the numbers May 21
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Financial time series forecasting. Whilst still using the CNN layered architecture, [7] demonstrates the benefit of replacing the softmax activation function with a linear support vector machine. The learning minimizes a margin-based loss instead of the cross-entropy loss. The . author. manages to achieve.

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However, when we look at financial time series, it is quite a common sense that some derived signals are useful for predictions too. For example, price and volume together can provide a better clue. Also some other technical indicators such as the moving average of different window size are useful too.

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