STIMP
0.1.0
Installation
Usage
Train baselines
Four representative coastal ocean area analysis
Supplementary main results
Imputation length (including half year and one-and-a-half years)
Prediction Length
Recurrent Prediction
Case study: predicted Chl_a of STIMP and baselines trained on imputed dataset
STIMP, MaskedAE and Lin-itp
Imputation of DINEOF with different t
Predicted Chl_a of STIMP and Climate mean
STIMP
Supplementary main results
View page source
Supplementary main results
Code for reproducing the main results presented in the supplementary
Imputation length (including half year and one-and-a-half years)
Half-year Imputation
One-and-a-half-years Imputation
Prediction Length
Different Prediction Horizons in PRE
Different Prediction Horizons in Chesapeake Bay
Different Prediction Horizons in Gulf of the Mexico
Different Prediction Horizons in Yangtze River Estuary
Recurrent Prediction
Recurrent Prediction
Case study: predicted Chl_a of STIMP and baselines trained on imputed dataset
Predicted Chl_a of STIMP and baselines trained on imputed dataset at three locations
STIMP, MaskedAE and Lin-itp
STIMP, MaskedAE and Lin-itp
Imputation of DINEOF with different t
STIMP vs DINEOF with different values of t
Predicted Chl_a of STIMP and Climate mean
Overall improvement of STIMP compared to two climate mean predictions
Predicted Chl_a of STIMP and two climate mean state at four locations