This is an example reprex using the AirPassengers data from the {datasets} package in base R.
I ran dput(AirPassengers) and pasted the output into an RScript, adding "AirPass <-" at the start of the first line, then the rest of the code and created the reprex. This can be very useful for someone trying to help you.
library(forecast)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
AirPass <- structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119,
104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114,
140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166,
171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196,
196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188,
235, 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267,
269, 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313,
318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355,
422, 465, 467, 404, 347, 305, 336, 340, 318, 362, 348, 363, 435,
491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 548,
559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606,
508, 461, 390, 432), .Tsp = c(1949, 1960.91666666667, 12), class = "ts")
fit <- hw(AirPass, seasonal = "multiplicative")
summary(fit)
#>
#> Forecast method: Holt-Winters' multiplicative method
#>
#> Model Information:
#> Holt-Winters' multiplicative method
#>
#> Call:
#> hw(y = AirPass, seasonal = "multiplicative")
#>
#> Smoothing parameters:
#> alpha = 0.3146
#> beta = 0.0071
#> gamma = 0.5977
#>
#> Initial states:
#> l = 120.3796
#> b = 1.7757
#> s = 0.9298 0.7946 0.9024 1.0451 1.1338 1.1388
#> 1.0529 0.9638 1.0349 1.0807 0.9854 0.9378
#>
#> sigma: 0.0407
#>
#> AIC AICc BIC
#> 1405.654 1410.511 1456.141
#>
#> Error measures:
#> ME RMSE MAE MPE MAPE MASE ACF1
#> Training set 1.256973 10.63256 7.790649 0.2182707 2.914411 0.2432275 0.2135914
#>
#> Forecasts:
#> Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
#> Jan 1961 445.8901 422.6577 469.1225 410.3592 481.4210
#> Feb 1961 418.9478 396.0288 441.8667 383.8963 453.9993
#> Mar 1961 466.4298 439.7182 493.1414 425.5780 507.2816
#> Apr 1961 496.1291 466.4627 525.7955 450.7583 541.4999
#> May 1961 507.1463 475.5546 538.7381 458.8309 555.4617
#> Jun 1961 575.6281 538.3478 612.9083 518.6129 632.6432
#> Jul 1961 666.6573 621.8494 711.4652 598.1295 735.1850
#> Aug 1961 658.4970 612.6386 704.3554 588.3627 728.6313
#> Sep 1961 550.0907 510.4559 589.7255 489.4745 610.7069
#> Oct 1961 491.7130 455.1069 528.3190 435.7289 547.6971
#> Nov 1961 418.8086 386.6330 450.9842 369.6003 468.0169
#> Dec 1961 463.7188 426.9948 500.4428 407.5543 519.8833
#> Jan 1962 478.5040 433.5276 523.4805 409.7185 547.2896
#> Feb 1962 449.4074 406.2454 492.5694 383.3969 515.4179
#> Mar 1962 500.1396 451.0781 549.2010 425.1065 575.1726
#> Apr 1962 531.7730 478.5135 585.0325 450.3196 613.2263
#> May 1962 543.3672 487.8249 598.9096 458.4226 628.3119
#> Jun 1962 616.4994 552.2059 680.7929 518.1710 714.8279
#> Jul 1962 713.7167 637.8042 789.6291 597.6185 829.8148
#> Aug 1962 704.7115 628.2918 781.1313 587.8376 821.5855
#> Sep 1962 588.4751 523.4340 653.5163 489.0033 687.9469
#> Oct 1962 525.8278 466.6127 585.0428 435.2661 616.3894
#> Nov 1962 447.7002 396.3464 499.0539 369.1614 526.2389
#> Dec 1962 495.5277 437.6488 553.4067 407.0096 584.0459
autoplot(fit)

Created on 2022-01-19 by the reprex package (v2.0.1)