特立尼达站点缺失风速数据imputation方法咨询及可行性探讨

Hey there! You’ve already worked through a really solid set of imputation methods for that 15-year hourly wind speed dataset—handling long time-series like this is no easy feat, so kudos for putting in that groundwork.

Here are some additional, targeted methods that could help you get more accurate imputations, especially tailored to the unique patterns in wind speed data:

• Seasonal Decomposition + Component-Wise Imputation
  Wind speed has super strong seasonal patterns—daily (like morning lulls vs. afternoon gusts), monthly, and annual. Try decomposing your time series into trend, seasonal, and residual parts using something like STL (Seasonal and Trend decomposition using Loess). Impute each component separately: use linear interpolation for the slow-moving trend, seasonal means for the repeating seasonal component, and nearest neighbor for the random residuals. Then recombine them. This shines when missing values fall into those predictable seasonal gaps.

• Time-Series Forecasting as Imputation
  Treat missing values as a forecasting problem—since wind speed is autocorrelated (past values predict future ones), these models work great:

  • SARIMA: The seasonal version of ARIMA is built for time-series with repeating cycles. It uses past observations and seasonal patterns to predict missing hourly values perfectly for data with clear diurnal/annual trends.
  • LSTM/GRU Neural Networks: These deep learning models excel at capturing complex, non-linear temporal patterns (like sudden storm winds or subtle seasonal shifts). Train one on your complete data segments to predict gaps—they’re especially useful for long, messy time-series where linear methods fall short.
  • Prophet: Developed for time-series with strong seasonality, it handles missing data natively and is super easy to implement. It’s a great pick if your wind data has clear daily or annual cycles (which most sites do).

• Spatial Imputation (if adjacent station data is available)
  If you can get hourly wind data from nearby weather stations, spatial methods can add a lot of accuracy:

  • Inverse Distance Weighting (IDW): Weight values from nearby stations based on how close they are—closer stations get more say in the imputed value.
  • Kriging: A geostatistical method that accounts for spatial autocorrelation (how wind speed varies across your region). It’s more precise than IDW if you can model the spatial variance of wind in your area.

• Domain-Specific Rule-Based Imputation
  Wind speed follows predictable domain patterns you can leverage:

  • For overnight missing values, use the typical diurnal minimum wind speed for that month/season.
  • For daytime gaps, average the wind speed from the same hour of day over the past week or year.
  • If you have other local meteorological data (temperature, pressure, humidity), build a multivariate imputation model (tweak your existing MICE setup to include these predictors!)—wind speed is tightly linked to pressure gradients and temperature differences, so these variables can fill in gaps more accurately.

• Hybrid Methods
  Mix and match methods for better results: For example, use SARIMA to get a baseline prediction, then adjust it with residual values from a nearest neighbor imputation. Or use an LSTM to capture complex patterns, then refine the output with seasonal decomposition.

One quick tweak for your existing MICE setup: Make sure you add time-series-specific predictors like hour of day, month, and lagged wind speed values. Standard MICE doesn’t account for temporal autocorrelation by default, so adding these will make your multiple imputations much more accurate.

内容的提问来源于stack exchange，提问作者L N