Explaining ARIMA forecasts [closed]

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Currently attempting to interpret the results of my forecast using an ARIMA model that was applied to time series data (Dataset below). The forecast attempted is for a year into the future. The data was fitted to an ARIMA(1,0,1) model after using auto.arima to see what the best model would be.

To give a little background on the dataset, it displays the amount of breaking & entering's that happened in Toronto by month between 2014 to 2021.

Plot results of the forecast: https://i.stack.imgur.com/j8IuE.png

What I used for my forecast:

#Convert timeseries
BEDATA_GROUPEDtssarima <- ts(BEDATA_GROUPED[,2], frequency = 12, start = c(2014, 1))
class(BEDATA_GROUPEDtssarima)

#Plot
forecast::autoplot(BEDATA_GROUPEDtssarima)

# Check autocorrelation: High autocorrelation and data is non stationary
acf(BEDATA_GROUPEDtssarima)

#Partial Autocorelation
pacf(BEDATA_GROUPEDtssarima)

#augmented test
adf.test(BEDATA_GROUPEDtssarima)

#auto.arima
beddatamodel <- auto.arima(BEDATA_GROUPEDtssarima,ic="aic",trace = TRUE)

#look at model
beddatamodel

#autocorelation for auto.arima
acf(ts(beddatamodel$residuals))

#Partial autocorrelation
pacf(ts(beddatamodel$residuals))

#Forecast
mybeddataforecast <- forecast(beddatamodel, level = c(95), h=12)

#Check forecast
mybeddataforecast

#plot
plot(mybeddataforecast)

#Check Accuracy
accuracy(mybeddataforecast)

DATA:

structure(list(occurrence_yrmn = c("2014-January", "2014-February", 
"2014-March", "2014-April", "2014-May", "2014-June", "2014-July", 
"2014-August", "2014-September", "2014-October", "2014-November", 
"2014-December", "2015-January", "2015-February", "2015-March", 
"2015-April", "2015-May", "2015-June", "2015-July&开发者_StackOverflow中文版quot;, "2015-August", 
"2015-September", "2015-October", "2015-November", "2015-December", 
"2016-January", "2016-February", "2016-March", "2016-April", 
"2016-May", "2016-June", "2016-July", "2016-August", "2016-September", 
"2016-October", "2016-November", "2016-December", "2017-January", 
"2017-February", "2017-March", "2017-April", "2017-May", "2017-June", 
"2017-July", "2017-August", "2017-September", "2017-October", 
"2017-November", "2017-December", "2018-January", "2018-February", 
"2018-March", "2018-April", "2018-May", "2018-June", "2018-July", 
"2018-August", "2018-September", "2018-October", "2018-November", 
"2018-December", "2019-January", "2019-February", "2019-March", 
"2019-April", "2019-May", "2019-June", "2019-July", "2019-August", 
"2019-September", "2019-October", "2019-November", "2019-December", 
"2020-January", "2020-February", "2020-March", "2020-April", 
"2020-May", "2020-June", "2020-July", "2020-August", "2020-September", 
"2020-October", "2020-November", "2020-December", "2021-January", 
"2021-February", "2021-March", "2021-April", "2021-May", "2021-June", 
"2021-July", "2021-August", "2021-September", "2021-October", 
"2021-November", "2021-December"), MCI = c(586, 482, 567, 626, 
625, 610, 576, 634, 636, 663, 657, 556, 513, 415, 510, 542, 549, 
618, 623, 666, 641, 632, 593, 617, 541, 523, 504, 536, 498, 552, 
522, 519, 496, 541, 602, 570, 571, 492, 560, 525, 507, 523, 593, 
623, 578, 657, 683, 588, 664, 582, 619, 512, 630, 644, 563, 654, 
635, 732, 639, 748, 719, 567, 607, 746, 739, 686, 805, 762, 696, 
777, 755, 675, 704, 617, 732, 609, 464, 487, 565, 609, 513, 533, 
505, 578, 526, 418, 428, 421, 502, 452, 509, 492, 478, 469, 457, 
457)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-96L))

My question:

(1) Based on the ending forecast and the steps completed to generate it, are there modifications that could be made to optimize my forecast? Transformation, differencing, etc.