/STE-for-Air-Quality-Prediction

Primary LanguageMATLAB

STE Model for Air Quality Prediction

Implement a novel and effective spatial-temporal ensemble model for air quality prediction.

  1. dat：Data of the model.

    • air_bj.mat: Air quality and meteorology data of one year, sampled once an hour, from 35 monitoring stations in Beijing. Location information(longitude and latitude) of stations.

  2. baselines: Baselines of our model.

    • Linear regression
    • Gaussian process
    • Regression tree
    • SVR, Neural network
    • Deep neural network
    • FFA model: The model proposed in [2].

  3. lib: Some relevant functions of our STE model.

    • granger_cause: Granger causality. To discover the spatial correlation.
    • libsvm-master: SVM & SVR. Need to install.
    • myKmeans: K-Means and fc-Means.
    • myNN: Neural network & Stack auto encoder.
    • myLSTM: LSTM.

  4. src: Proposed STE model.

    • T.m: Only temporal part.
    • ST.m: Only spatial and temporal parts.
    • SE.m: Only spatial and ensemble parts.
    • STE.m: Spatial-temporal ensemble model.

Reliance:

  1. libsvm

Reference:

  1. Prediction as a candidate for learning deep hierarchical models of data (Palm, 2012)
  2. Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, T. Li, Forecasting fine-grained air quality based on big data, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2015, pp. 2267–2276.