<ul><li>Topological Data Analysis (TDA) is used for extracting features from complex data structures.</li><li>Integration of TDA with time-series prediction faces challenges related to temporal dependencies and computational bottlenecks.</li><li>The Topological Information Supervised (TIS) Prediction framework proposes using neural networks and CGANs to generate synthetic topological features.</li><li>TIS models, TIS-BiGRU and TIS-Informer, outperform conventional predictors in capturing short-term and long-term temporal dependencies.</li></ul>

Time-Series Forecasting via Topological Information Supervised Framework with Efficient Topological Feature Learning

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