Time Series Forecasting Models for Prediction of Conjunctivitis Disease Cases
Keywords:
conjunctivitis, time series, forecasting, seasonal naïve, neural networks, mean forecastAbstract
In Recent times, Machine learning is a powerful technique for the data analysis and for making a future prediction. There are many existing forecasting models which are useful for the prediction in different areas. Acute conjunctivitis, commonly known as "pink eye", is one of the most common eye infections, particularly among schoolchildren. Because of its highly contagious nature everyone is susceptible especially those in crowded places such as kindergartens, indoor amusement parks and swimming pools. Hence as a precautionary measure, there is an imperative need to predict the future possibilities of Conjunctivitis cases. Therefore, in this manuscript, machine learning based forecasting models are used for prediction of conjunctivitis cases in Hong Kong. Analysis is conducted on the data of past years conjunctivitis cases in Hong Kong. Mean Forecast, Seasonal Naive, Auto ARIMA, Neural Network techniques are used for analysis and forecasting. The surpassing model is adopted based on the accuracy factor. Accuracy of the models are compared with respect to Root Mean Square Error and Auto Correlation Function. Result analysis reveal that the neural network model produces least error and hence is the best prediction model for our dataset in terms of accuracy.
How to cite this article:
Verma S, Sharma N. Time Series Forecasting Models for Prediction of Conjunctivitis Disease Cases. J Engr Desg Anal 2021; 4(1): 34-38.
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