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In recent years, with the rapid deployment of various Internet of Things (IoT) devices, it becomes a crucial and practical challenge to enable real-time search for objects, data, and services in the Internet of Everything. The IoT data prediction model can not only provide solutions for the real-time acquisition of the IoT sensor data but also provide more meaningful applications than the traditional IoT event detection model. In this paper, we use the complex time series formed by various types of sensors to establish a multi-dimensional feature selection model and a dynamic sensor-data prediction model. Compared with the traditional data prediction model, our model improves the accuracy and stability of the long-term prediction results of the IoT sensor data. Finally, we evaluate our prediction model using the Intel Berkeley Research Lab sensor data with an accuracy of over 98% and 92% accuracy on the Chicago Park District weather&water data. INDEX TERMS IoT sensor data prediction, complex time series, multi-dimensional feature selection.

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