In the previous article "Neural Network in Practice: Least Squares", we looked at how, in very simple cases, we can find an equation that best describes the data set we are using. The equation that was formed in this system was very simple, it used only one variable. We've already shown how to do the calculation, so we'll get straight to the point here. This is because the mathematics used to create an equation based on the values available in the database requires significant knowledge of ...
In the previous articles, we discussed the FEDformer method that uses the frequency domain to find patterns in a time series. However, the Transformer used in that method can hardly be referred to as a lightweight model. Instead of complex models that require large computational costs, the paper "FITS: Modeling Time Series with 10k Parameters" proposes a method for the frequency interpolation of time series (Frequency Interpolation Time Series - FITS). It is a compact and efficient solution for ...
A common approach when processing time series is to keep the original arrangement of the time steps intact. It is assumed that the historical order is the most optimal. However, most existing models lack explicit mechanisms to explore the relationships between distant segments within each time series, which may in fact have strong dependencies. For example, models based on convolutional networks (CNN) used for time series learning can only capture patterns within a limited time window. As a result, ...
The moving average cross-over is probably one of the oldest existing trading strategies. The Moving Average Convergence Divergence (MACD) is a very popular indicator is built on top of the notion of moving average cross-overs. There are many new members of our community who may be curious to know the predictive power of the MACD indicator, in their search to build the best trading strategy possible. Additionally, there are seasoned technical analysts who utilize the MACD in their strategies ...
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