A comparison of data imputation methods using Bayesian compressive sensing and Empirical Mode Decomposition for environmental temperature data

作者：Williams D Alexandra; Nelsen Benjamin; Berrett Candace; Williams Gustavious P^*; Moon Todd K

来源：Environmental Modelling & Software, 2018, 102: 172-184.

DOI：10.1016/j.envsoft.2018.01.012

• Environmental data imputation
• Bayesian compressive sensing
• Empirical mode decomposition

We present two Bayesian compressive sensing (BCS) imputation methods, BCS-on-Signal and BCS-on-IMF, and compare to temporal and spatio-temporal methods. We build sparse BCS models using available data, then use this sparse model for imputation. Most BCS applications have the sparse data distributed across the computational space, in our adaptation the "sparse" data are outside the reconstruction space. We used 30 years of temperature data and created gaps of 1% (similar to 110 days), 5% (similar to 1.5 years), 10% (similar to 3 years), and 20% (similar to 6 years). Performance was not sensitive to gap size with RMSE slightly above 6 degrees C for the BCS-on-Signal and Temporal models, the two best methods. The methods which only required data from the target station performed as well as, or better than, the spatio-temporal model which requires data from surrounding stations. Visually the BCS-on-IMF results seem to better represent longer-period random temporal fluctuations while having poorer performance metrics.

• 出版日期2018-4