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Time Series Analysis with Cryptocurrency

Ankit Raj
Student, Computer Science and Engineering, Parul University, Vadodara
|ar1046662@gmail.com

Abstract
Cryptocurrency markets are characterized by extreme volatility, rapid price fluctuations, and complex nonlinear behavior,making accurate forecasting a significant challenge for investors, analysts, and researchers. This study investigates the application of Time Series Analysis techniques to model and predict cryptocurrency prices using historical market data. Both traditional statistical approaches, such as the AutoRegressive Integrated Moving Average (ARIMA) model, and advanced deep learning methods, including Long Short-Term Memory (LSTM) networks, are implemented to capture underlying temporal patterns. The dataset consists of daily open, high, low, close prices, and trading volume obtained from reliable financial data sources. Data preprocessing steps such as handling missing values, normalization, stationarity testing using the Augmented Dickey-Fuller test, and time series decomposition are performed to ensure model efficiency and accuracy. Exploratory Data Analysis (EDA) is conducted to identify trends, seasonality, and volatility characteristics. Model performance is evaluated using statistical metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The comparative analysis demonstrates that while ARIMA performs adequately for short-term forecasting, LSTM models provide superior performance in capturing nonlinear and long-term dependencies within cryptocurrency price movements. However, external factors such as market sentiment and regulatory changes continue to influence prediction accuracy. This research contributes to a better understanding of cryptocurrency forecasting techniques and highlights the effectiveness of deep learning approaches in financial time series analysis.
Keywords: Cryptocurrency, Time Series Analysis, ARIMA, LSTM, Price Prediction