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How to code Temporal Distribution Characterization (TDC) for time series?
There is no need to express the necessity of forecasting time series data by deep learning methods. I did provide an article about how we can handle the variations in statistical features (particularly, distribution perspective), which can lead us to a disaster. The model proposed is named AdaRNN, including two main initiatives, TDC and TDM. This article is going to illustrate TDC in detail.
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I’m not gonna speak about everything from scratch, if you want, you can check my previous article, “Adaptive Learning for Time Series Forecasting”. Let’s go through TDC:
Figure 1 shows the architectural view of using TDC, it separates the dataset into intervals with the most possible diversity. This makes the model robust on distribution variations. The main point of TDC is maximizing the entropy (Equation 1, objective), as you can see in Figure 1 and Equation 1.
In Equation 1, we need to make the borders for Di not too large or not too small because it can be led to a failure of capturing the information out of the data.
