I did the forecast of the monthly variation of an indice and, base on the las observed indice and the forecasted variations, I want to get the corresponding forecast of the indice.
I have four variables:
-
date: the month -
f: indicates if the observation is an observed value ("observado") or a forecast result. When it is a forecasted result this variable indicate the model used to get the forecast ("auto.arima", "ets", "bats") -
var: the monthly variation -
indice: target variable that should be fill using this formula indice_t = indice_{t-1} * (1 + var_t)
Example data
df <- structure(list(date = structure(c(18109, 18140, 18170, 18201,
18231, 18262, 18293, 18140, 18170, 18201, 18231, 18262, 18293,
18140, 18170, 18201, 18231, 18262, 18293), class = "Date"), f = c("observado",
"auto.arima", "auto.arima", "auto.arima", "auto.arima", "auto.arima",
"auto.arima", "ets", "ets", "ets", "ets", "ets", "ets", "bats",
"bats", "bats", "bats", "bats", "bats"), var = c(0.00164468135353065,
0.000789326024367794, 0.000378818408445376, 0.000181804960367329,
8.72530026980781e-05, 4.18750207059747e-05, 2.00969285285633e-05,
-0.00319710269210977, -0.00322906819558889, -0.00326022342842121,
-0.00329058892957686, -0.00332018471739832, -0.00334903030279746,
-0.000505340580329043, -0.00101461802017405, -0.00101461802017405,
-0.00101461802017405, -0.00101461802017405, -0.00101461802017405
), indice = c(118.759, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA)), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -19L))
This is my firs question here, thanks in advance for your answer!
Edit
I edited the formula beacuse I was not exactly what I wanted to do.
At last I found a way to get the job done. But, even I keep the code in the tidyverse ground, it is not that clear and elegant as it could be.
df %>%
mutate(
indice = ifelse(
is.na(indice),
last(indice[f == "observado"]),
indice)
) %>%
group_by(f) %>%
mutate(
var_cum = (1+ var),
var_cum = cumprod(var_cum),
indice = ifelse(
!f == "observado",
indice * var_cum,
indice
)
)
date f var indice var_cum
1 2019-08-01 observado 1.644681e-03 118.7590 1.0016447
2 2019-09-01 auto.arima 7.893260e-04 118.8527 1.0007893
3 2019-10-01 auto.arima 3.788184e-04 118.8978 1.0011684
4 2019-11-01 auto.arima 1.818050e-04 118.9194 1.0013505
5 2019-12-01 auto.arima 8.725300e-05 118.9298 1.0014378
6 2020-01-01 auto.arima 4.187502e-05 118.9347 1.0014798
7 2020-02-01 auto.arima 2.009693e-05 118.9371 1.0014999
8 2019-09-01 ets -3.197103e-03 118.3793 0.9968029
9 2019-10-01 ets -3.229068e-03 117.9971 0.9935842
10 2019-11-01 ets -3.260223e-03 117.6124 0.9903448
11 2019-12-01 ets -3.290589e-03 117.2253 0.9870860
12 2020-01-01 ets -3.320185e-03 116.8361 0.9838087
13 2020-02-01 ets -3.349030e-03 116.4449 0.9805139
14 2019-09-01 bats -5.053406e-04 118.6990 0.9994947
15 2019-10-01 bats -1.014618e-03 118.5786 0.9984806
16 2019-11-01 bats -1.014618e-03 118.4582 0.9974675
17 2019-12-01 bats -1.014618e-03 118.3381 0.9964554
18 2020-01-01 bats -1.014618e-03 118.2180 0.9954444
19 2020-02-01 bats -1.014618e-03 118.0980 0.9944344