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Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. It is necessary to extract these knowledge from hydrological data set, which can provide more valuable hydrological information and be useful for future hydrological forecasting. Data mining based on time series is widely used currently. There are some techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data such as hydrological big data set. Some important problems are high fitting error after dimension reduction and low accuracy of mining results. In this work we propose a new idea to solve the problem of hydrological anomaly mining based on time series. The idea combines time series symbolization with distance measure. It proposes Feature Points Symbolic Aggregate Approximation (FP SAX) to improve the selection of feature …