Abstract: Location-based services have become a part of human life style,and various mobile terminal devices generate a large amount of temporal and spatial contextual user information,which can be used to predict the user's next footprint.Some solutions have been proposed to predict the user's next footprint,including recursive motion function (RMF),matrix factorization (MF),differential autoregressive moving average model (ARIMA),Markov chain (MC),and personalization Markov chain (FPMC),Kalman filter (KF),Gaussian mixture model and tensor decomposition (TF).In addition,deep neural network methods such as ST-RNN,POI2Vec,DeepMove,VANext,etc.can also be used to predict the user's next footprint.These methods use recurrent neural networks (RNN) to capture sequential motion patterns from human activities.However,existing methods use some artificially set thresholds to segment human mobility data for user movement pattern learning.The artificial fixed time stamp setting not only introduces human subjective factors,but also ignores the differences between different users.It may lead to deviations in the movement pattern.The existing methods for the extraction of user trajectory features are too singular,and a single feature ignores a lot of potential user trajectory information.The trajectory prediction model based on adaptive timestamp and multi-scale feature extraction (AMSNext) aims to combine the time statistical characteristics of historical trajectory data for the first time,adaptively define a personalized timestamp for each user,and focus on the differences between different user motion modes.Combined with time series feature extraction methods to extract user trajectory features at multiple scales,and at the same time,to achieve multi-scale feature dimension unification,normalized causal embedding will be used to embed features in vector.Experiments show that the model can achieve higher prediction accuracy.

Key words: Adaptive timestamp, Feature extraction, Normalized embedding, Time series, Trajectory prediction