I am having a weekly data and want to forecast values based on available actuals in R using timeseries model.

I tried to convert the data to a timeseries data using (ts) but couldnt.

Weekly

Income

1/8/2018

1220

1/15/2018

2200

1/22/2018

8800

1/29/2018

7743

2/5/2018

4432

2/12/2018

56789

2/19/2018

95643

2/26/2018

2200

3/5/2018

23400

3/12/2018

3340

3/19/2018

2098

3/26/2018

12098

4/2/2018

12060

4/9/2018

10980

4/16/2018

202987

4/23/2018

40003

4/30/2018

30009

5/7/2018

3480

5/14/2018

40090

5/21/2018

6800

5/28/2018

40088

t_data<-ts(mydata1_subset2$income,frequency = 52,start = c(2018,1))
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any idea how to convert the data set to a timeseries data based on weekly and any model to predict the value for future.

I am not sure how to include this in model, even when we put this in a time series model either Arima or Tbats
the result is totally different . Any idea

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

The data below gives me weekly time series but which would be a good model to fit this weekly data and predict the value for next 12 months weekly prediction.

I was hoping that ksasi would realise he didnt share the data in the recommended way, which I previously pointed him to. It seemed a reasonable way to justify reading the guide...

Thanks nigrahamuk for your support. I tried exporting the timeseries dataset but it is not in correct format hence I just provided the data directly. Meanwhile I learned that Frequency =52 plays trick in weekly timeseries. Hence I able to understand the TS data now.. but anyone can suggest, how "Holt winters" method works to predict immediate week from actual data.

Example:
I am giving timeseries data from 2018 Jan to 2020 Aug on weekly basis and I want to predict from 2020 Sept to next 60 weeks.

Whether Holt winters is good or any other method will do

Unless your data has very small variability, for a forecast horizon of 60 weeks none of the models will be satisfactory. This is because the confidence intervals will quickly grow beyond the range of historical data and may even become negative.