I liked the way you put across your question!

I think we cannot cannot say in specific will work well with data, it is most likely trail and error method, If ARIMA is not performing well and assuming that there is no trend in data then you can use AR, Exponential Smoothening. These are basic techniques but as you know in many scenarios basic models can explain better than complex models.

These both works well in such scenarios. Give a try.

It would be nice if you can share some graphs which can explain us a bit more about the how the data is, noise etc. I mean if you have anything from your exploratory analysis.

Hope my answer is helpful!

I liked the way you put across your question!

I think we cannot cannot say in specific will work well with data, it is most likely trial & error method, If ARIMA is not performing well and assuming that there is no trend in data then you can use AR, Exponential Smoothening. These are basic techniques but as you know in many scenarios basic models can explain better than complex models.

These both works well in such scenarios. Give a try.

It would be nice if you can share some graphs which can explain us a bit more about the how the data is, noise etc. I mean if you have anything from your exploratory analysis.

Hope my answer is helpful!

I liked your question!

I think we cannot cannot say in specific will work well with data, it is most likely trail and error method, If ARIMA is not performing well and assuming that there is no trend in data then you can use AR, Exponential Smoothening. These both work well in such scenarios. Give a try.

It would be nice if you can share some graphs which can explain us a bit more about the how the data is, noise etc. I mean if you have anything from your exploratory analysis.

Hope my answer is helpful!

I liked the way you put across your question!

I think we cannot cannot say in specific will work well with data, it is most likely trail and error method, If ARIMA is not performing well and assuming that there is no trend in data then you can use AR, Exponential Smoothening. These are basic techniques but as you know in many scenarios basic models can explain better than complex models.

These both works well in such scenarios. Give a try.

It would be nice if you can share some graphs which can explain us a bit more about the how the data is, noise etc. I mean if you have anything from your exploratory analysis.

Hope my answer is helpful!