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Cryptocurrency Prediction by Kautilya

Given the cryptocurrencies’ data, we aim to forecast the future cryptocurrencies’ prices so as to execute profitable trades. We show that the cryptocurrencies’ prices also exhibit desirable properties such as stationarity and mixing. Some classical time series prediction models that exploit this behavior, such as “Arima” models produce poor predictions and also lack good probabilistic interpretations. We have introduced a theoretical framework in the 1st place and for predicting and trading prices of the cryptocurrencies for future and based on that we have designed our model which is based on “Neural Network” model which can give better prediction values as compared to the other models.


6 thoughts on “Cryptocurrency Prediction by Kautilya

  1. 2

    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.

    1. 1

      Yep, It wont give the predictions for the dates because it will give 5 day predictions of all cryptocurrencies starting from 25th Jan 2018. But we can predict for the given dates by varying the value of k. That’s how we predicted 30th Jan.

  2. 1

    1. You may want to include some evaluation metrics for your models both on train & test sets.
    2. On the data prep part – it is not the best solution to just remove rows where you see missing values because it is time-series data and could seriously bias your next steps.
    3. Assumption you have made about the “large number of missing values” is probably poor. Do you have any data/metric you used to prove it?
    4. You may want to include more detailed explanation why the data is not continuous (here is a link on discrete and continuous data
    5. How you would rank your model? What are the metrics you used?

    1. 1

      Sorry but I feel there is some misinterpretation. We have not deleted any rows rather than we have imputed using “na.kalman” of library “imputeTS” using “auto.arima” model. There were lot of missing rows and many time interval were missing.

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