nabebako / crypto-price-prediction-model

A comparison between different approaches for predicting cryptocurrencies' prices

Overview

This is a high school project in year 11 computer class. The project revolves around comparing different models' performance in regard to predicting the prices of cryptocurrencies.

Instructions

Choose one or several of your favorite cryptocurrencies. Develop a couple of mathematical models in python which attempt to predict the future prices of these cryptocurrencies. After your models have been developed, collect data in regards to how close each model was to correctly predicting the cryptocurrency price. Write a report discussing the pros and cons of your models; discuss which model performed the best.

Objectives

We will try to assert the accuracy of various models when doing analysis on crypto prices. We had gathered four datasets from CoinDesk: ADA, ETH, DOGE, and SOL, each having differnt trends and volitility. We will conduct weekly forecasting analysis on said datasets using 5 approches listed below:

• Baseline - a simple model that predicts based on prices from a specific window of time prior.

• Polynomial regression - a linear or polynomial regression line use predict future values.

• KNN (k-nearest neighbors) regression - uses similar traits of a given day to determine prices.

• ARIMA (Autoregressive integrated moving average) - consider correlation of lags (passed values) to predicts values.

• AdaBoost (Adaptive boost) regression - a collection of weak learners that cover each other weaknesses.

Implementation will be done using python in Jupyter Notebook in combination with third party libraries: pandas, NumPy, Matplotlib, scikit-learn and statsmodels. We will measure accuracy by measuring the mean absolute precentage error (MAPE) on a 70:30 train-test-split ratio datasets with rolling forecasting - refitting modes every time a new day is predicted with that day's values.

Results

Mean absolute percentage error chart using 70:30 train-test ratio with rolling forecast.

Coin	ADA	ETH	DOGE	SOL
ARIMA	3.46%	4.0%	3.62%	3.46%
Polynomial Regression	3.89%	4.87%	4.47%	4.68%
KNN Regression	1.36%	1.83%	1.44%	1.93%
Adaboost Regression *	~ 3.95%	~ 5.58%	~ 7.64%	~ 4.55%
Baseline	4.94%	6.38%	8.93%	6.80%

* Adaboost models' accuracy changes everytime a model is created; even with the same training data.