FORECASTING OZONE CONCENTRATION DATA: ARIMA V/S LSTM

Forecasting time series data is an important subject in climate monitoring, weather forecasting and pollution level estimation. Traditional techniques used are univariate Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA). ARIMA models have proven their superiority in precision and accuracy of predicting the next lags of time series. Due to the recent advancements in computational power of computers we are able to use data intensive techniques such as deep learning. The question explored in this paper is that whether or not the deep learning-based algorithms, like “Long Short-Term Memory (LSTM)”, is better or not when compared to the traditional algorithms when used for time series forecasting of weather data. The study conducted here shows that ARIMA outperformed algorithms such as LSTM for short-term weatherrelated data prediction. The ARIMA model provided a decent reduction in error rate when compared to the LSTM approach. Also, there was noticeable difference in the overall processing time for both the algorithms with the ARIMA model finishing first, thereby providing reduction in the running time required for such type of operations.