metodinikolov

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Cryptocurrency Prediction by Kautilya

In general, the provided code would not give predictions for the required dates:
30.01.2018, 06.02.2018, 20.02.2018, 09.03.2018, 18.03.2018

That said, the code is well documented and seems to be fairly easy to be extended to include the required time period.

It will test for the goodness of the predictions.

A bit more explanatory text what the nnetar function does and how it was used would be beneficial.

The input data used has not been provided, so I am unable to run the code as is.

UNWE Article – Crypto Datathon

It would have been great if you had used what you learned in the first half about log-returns and how they behave in the prediction’s second half. As it stands it seems that you are using the prices to do the prediction, rather than any modification of it.
There also seem to be several different types of predictions – ARIMA(1, 1, 1) (which some what handles prices vs log-returns) and AR(1) model (which is an ARIMA(1, 0, 0)) – correct me if i am wrong, but you haven’t done any comparisons between the two models? As they are nested, readily available statistical tests could have been used for that part. Also, the final predictions are from which model?

Prediction Model for Crypto Currency in R

Your code is self sufficient (i.e. all inputs are present or part of the task supplied) which is a big plus. That said, there seems to be issues with the code, which makes it executable ( for example, variable pred_et is never defined)

In general, the provided code would not give predictions for the required dates:
30.01.2018, 06.02.2018, 20.02.2018, 09.03.2018, 18.03.2018

However, it seems that it can be changed to do that.

You are missing one of the currencies, because of what is most probably typo – the last selected currency is “146” rather than the required “1465”.

I like that you have formed three different predictions, but you do not try to explain which one is better – even simple comparisons between the three would increase the usefulness of the output.
Linked with this, the statistical tests are missing that check goodness of the predictions.

By KrYpToNiAnS

Some comments and feedback from me. In general, the provided code would not give predictions for the required dates:
30.01.2018
06.02.2018
20.02.2018
09.03.2018
18.03.2018

It will also not check how good the prediction was.

Some other comments follow:

1. I cannot run the code as the input data that you have used is missing.
2. It would be beneficial to all reading to better explain what neural network you are using.
3. The specifying of why the given parameters to nnetar function have been chosen would have been nice as well. For example, by default it assumes seasonality in the data – how this seasonality relates to the 5-min steps?
4. In the same vein, explaining what algorithm was used for inferring missing data points is necessary.
5. When you are making a following prediction after the first (for say t2), you seem to be using your prediction for t1, rather than the true data point from t1. Thus your prediction for t2 would be worse than it needs to be.