- Subhabaha Pal (firstname.lastname@example.org)
- Suprio Dutta (email@example.com)
- Faraz Ahmad (firstname.lastname@example.org)
- Prankrishna Dolai (email@example.com)
- Sudarshan Mishra (firstname.lastname@example.org)
- Nitin Khandare (email@example.com)
- Microsoft Excel
The goal is to build a successful investing/trading model on the cryptocurrency markets. The data consists of time-series of various cryptocurrencies (in 5-minute steps) prices and 24 hour volumes.
- Data provided in csv format
- Exploring dataset’s structure
- Identifying discrepancies in the dataset
- Identifying the subsets of data that modelling would be based on
- Raw Data:
- Price_data à Data type: Time Series à Data Format: CSV
- Data have more than 1600 cryptocurrencies with transaction details
- Data provided between 17th JAN 2018 11:25 to 24th MAR 2018 13:15
- Missing values in the dataset were identified and were imputed using “imputeTS” package.
- Converted the data to a time series by giving the frequency as 24 hours multiplied by 5 minutes.
- Variable reduction was done
- Here we have splited data base on traning and testing data set
- We have used here the neural network model
- By using forecast package we use the nnetar() function for forecasting time series
- This model makes the data stationary and then forecast it based on observation.
- Divided the data into two parts namely training and test data set.
- Fed the model with the train data set that helped the model to learn something about the data.
- Predicted the values present in the train data set.
- Compared the Predicted values with the Observed values and calculated the RMSE( Root Mean Square Error).
- Lastly we have selected the model which has the least RMSE Value and higher Accuracy.
- Ran the algorithm on all the test data and created multiple CSV files with the required structure.
- Also did a manual check on the results with lowest confidence score to detect errors (and, as expected, found some which we manually corrected).