ARIMA MODEL FOR FORECASTING THE DATA ANALYSIS
Volume: 13 - Issue: 06 - Date: 01-06-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU388 | Page No.: 107-112
Author: Chandni Tiwari
Co- Author: Dr .Sneha Soni
Abstract:-The COVID-19 pandemic has highlighted the critical need for accurate disease forecasting models to guide
public health interventions and policy decisions. This thesis investigates the application of the Autoregressive Integrated
Moving Average (ARIMA) model for forecasting the spread of COVID-19. The ARIMA model, known for its robustness in
time series analysis, is utilized to predict daily confirmed cases, recoveries, and deaths across various geographical
regions.
The study begins with a comprehensive data preprocessing phase, addressing issues such as missing values, outliers, and
the need for stationary time series data. Subsequently, we fit the ARIMA model to historical COVID-19 data, optimizing
its parameters using techniques such as grid search and cross-validation to ensure the best predictive performance.
Our findings demonstrate that the ARIMA model can effectively capture the temporal dynamics of COVID-19 spread,
offering reliable short-term forecasts. The model's performance is evaluated using standard metrics such as Mean
Absolute Error (MAE) and Root Mean Squared Error (RMSE), and results are compared against other forecasting
methods including exponential smoothing and machine learning-based approaches.
Key Words:-COVID-19, ARIMA, Disease Forecasting, Time Series Analysis, Public Health, Predictive Modeling, ARIMAX.
Area:-Engineering
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