I have a daily time series data of inbound call centre of last 10 months and i need to forecast for next two months. My all future forecasts are repeating after a week i.e. values of 2nd,3rd and 4th week are same as first week which is not the case as in the series historically.

```
data_ts <- ts(data$Chats_Number,start = decimal_date(as.Date("2017-01-27")),frequency = 7) 

```

Fourier terms are used to capture seasonality and k selected based on min AICc

```
xreg <- fourier(data_ts,K=3)
fit <- auto.arima(data_ts,xreg = xreg,seasonal = FALSE,lambda = 0)

summary(fit)
Series: data_ts 
Regression with ARIMA(1,1,2) errors 
Box Cox transformation: lambda= 0 

Coefficients:
      ar1      ma1      ma2     S1-7     C1-7     S2-7    C2-7    S3-7     C3-7
  -0.5743  -0.0841  -0.4290  -0.4538  -0.0843  -0.0276  0.2803  0.1604  -0.0427
s.e.   0.4588   0.4491   0.2998   0.0164   0.0164   0.0154  0.0154  0.0144   0.0144

sigma^2 estimated as 0.04724:  log likelihood=35.71
AIC=-51.42   AICc=-50.64   BIC=-14.61

Training set error measures:
               ME     RMSE     MAE        MPE     MAPE      MASE       ACF1
Training set 10.49613 66.75743 39.8977 0.08827764 12.58295 0.6308544 0.08468087 

forecasts <- forecast(fit,xreg = fourier(data_ts,K = 3,h=60)) 
forecast

Point Forecast    Lo 80     Hi 80     Lo 95     Hi 95
 2059.786       733.0478 521.5679 1030.2763 435.57098 1233.6888
 2059.928       675.4585 475.5280  959.4473 394.90127 1155.3372
 2060.071       392.7527 273.6144  563.7669 225.96364  682.6528
 2060.214       256.1152 176.6352  371.3587 145.09729  452.0760
 2060.357       737.4148 503.5734 1079.8437 411.50345 1321.4484
 2060.500       765.9576 518.0710 1132.4530 421.20582 1392.8844
 2060.643       778.4533 521.6047 1161.7794 421.97730 1436.0714
 2060.786       733.3445 486.8999 1104.5270 391.99627 1371.9368
 2060.928       675.3015 444.3614 1026.2639 356.05553 1280.7893
 2061.071       392.8052 256.2159  602.2105 204.34812  755.0640
 2061.214       256.0956 165.6139  396.0110 131.48760  498.7918
 2061.357       737.4473 472.8948 1149.9989 373.77846 1454.9487
 2061.500       765.9382 487.1177 1204.3523 383.33761 1530.4038
 2061.643       778.4646 491.0779 1234.0348 384.79543 1574.8814
 2061.786       733.3384 458.9319 1171.8190 358.08996 1501.8157
  -              -             -      -      -          - 

```

Is this because i had less data or is there any way around?

Edit: Model summary added,and plot and data [![enter image description here](https://i.stack.imgur.com/ArRbJ.png)](https://i.stack.imgur.com/ArRbJ.png)

```
library(forecast)
library(lubridate)
library(tseries)
library(fpp2)

a <- paste0(c("30 97 233 211 199 206 203 118 63 241 207 195 177 179 80 84 228 224 220 217 144 92 81 165 191 307 289 271 181 144 367 371 352 386 362 219 126 431 403 382 343 362 220 154 404 321 383 331 325 199 125 346 387 338 349 336 178 144 347 377 396 332 285 167 144 403 353 333 302 306 201 109 422 355 339 372 357 163 106 382 310 357 299 309 175 134 354 374 390 366 347 169 107 386 276 357 319 352 179 121 391 364 414 368 317 395 127 573 514 646 639 499 329 185 546 570 592 561 492 221 143 222 549 610 577 468 243 161 566 537 565 563 501 285 135 549 469 543 465 457 247 147 508 511 505 498 428 223 178 469 565 521 459 423 231 138 462 416 169 395 419 243 166 494 522 520 491 473 292 181 460 471 596 545 486 267 143 457 468 458 433 454 255 160 425 434 457 416 435 561 232 510 620 744 676 614 305 207 581 641 620 527 507 274 153 424 489 485 433 423 278 195 484 546 568 497 448 226 161 237 520 584 532 490 255 177 550 537 508 474 450 249 135 427 441 462 372 340 233 152 404 436 416 384 379 196 134 396 402 413 373 355 203 104 384 452 407 381 359 98 126 429 422 428 398 380 247 143 430 427 459 437 407 215 111 445 465 466 422 446 247 130 375 410 444 369 170 683 260 565 789 772 774 805"),collapse = NULL)
a <-  as.numeric(unlist(strsplit(a," ")))
data_ts <- ts(a,start = decimal_date(as.Date("2018-01-27")),frequency = 7)
autoplot(data_ts)

```