Linear regression using time lagged predictors (independent variables) for forecasting purpose

Question

I'm working on forecasting the Monthly Average Precipitation of a geographical region in India (Assam and Meghalaya subdivision). For this purpose, I'm using the Monthly Average Air Temperature data and Monthly Averaged Relative Humidity data (which I extracted and averaged it spatially from the netCDF4 file for this geographical region present on the NOAA website) as the independent variables(predictors).

For the forecasting purpose, I want to model a linear regression with Precipitation as the dependent variable and "Air Temperature" and "Relative Humidity" data as the independent variables such that they're having a time-lagged effect in the regression.

The Linear regression equation should look like:

Please follow this link for the equation

Here, "Y" is Precipitation, "X" is Air Temperature and "Z" is Relative Humidity.

The sample "Training data" is as follows:

   ID       Time Precipitation Air_Temperature Relative_Humidity
1   1 1948-01-01           105        20.31194          81.64137
2   2 1948-02-01           397        21.21052          80.20120
3   3 1948-03-01           594        22.14363          81.94274
4   4 1948-04-01          2653        20.79417          78.89908
5   5 1948-05-01          7058        20.43589          82.99959
6   6 1948-06-01          5328        18.10059          77.91983
7   7 1948-07-01          4882        16.63936          76.25758
8   8 1948-08-01          3979        16.56065          76.89210
9   9 1948-09-01          2625        16.95542          76.80116
10 10 1948-10-01          2578        17.13323          75.62411

And a segment of "Test data" is as follows:

        ID       Time Precipitation Air_Temperature Relative_Humidity
    1  663 2003-03-01           862        21.27210          79.77419
    2  664 2003-04-01          1812        20.44042          79.42500
    3  665 2003-05-01          1941        19.24267          79.57057
    4  666 2003-06-01          4981        18.53784          80.67292
    5  667 2003-07-01          4263        17.21581          79.97178
    6  668 2003-08-01          2436        16.88686          81.37097
    7  669 2003-09-01          2322        16.23134          77.63333
    8  670 2003-10-01          2220        17.40589          81.14516
    9  671 2003-11-01           131        19.01159          79.15000
    10 672 2003-12-01           241        20.86234          79.05847

Any help would be highly appreciated. Thanks!

Answer 1

Reacting to your clarification in the comments, here is one of many ways to produce lagged variables, using the lag function within dplyr (I am also adding a new row here for later forecasting):

df %>%
   add_row(ID = 11, Time = "1948-11-01") %>%
   mutate(Air_Temperature_lagged = dplyr::lag(Air_Temperature, 1),
          Relative_Humidity_lagged = dplyr::lag(Relative_Humidity, 1)) -> df.withlags

You can then fit a straightforward linear regression using lm , with Precipitation as your dependent variable and the lagged versions of the two other variables as the predictor:

precip.model <- lm(data = df.withlags, Precipitation ~ Air_Temperature_lagged + Relative_Humidity_lagged)

You could then apply your coefficients to your most recent values in Air_Temperature and Relative_Humidity to forecast the precipitation for November of 1948 using the predict function.

predict(precip.model, newdata = df.withlags)
  1        2        3        4        5        6        7        8        9       10       11 
  NA 2929.566 3512.551 3236.421 3778.742 2586.012 3473.482 3615.884 3426.378 3534.965 3893.255 

The model's prediction is 3893.255 .

Note that this model will only allow you to forecast one time period into the future, since you don't have more information in your predictors.