Title: A Comparative Study of ARIMA and LSTM in Forecasting Time Series Data
Abstract: Forecasting time series data is an important subject in economics and business where the Autoregressive Integrated Moving Average (ARIMA) has been extensively used despite its weaknesses, from requiring a minimum number of data points to the assumed linearity of data. With recent advancement, the Long Short-Term Memory (LSTM) shows potential to address such weaknesses. This research is aimed to identify a more suitable model in handling irregular data. Performance metrics used are the model accuracy measured with RMSE and the model run-time performance measured with the Python Timeit library. This research concluded that LSTM is more accurate than ARIMA (RMSE of ARIMA 0.144887 to LSTM 0.051828) in a shorter dataset of 36 data points and this result is reverted in longer dataset of 228 data points (RMSE of ARIMA 0.006949 to LSTM 0.036025). In terms of run-time performance, ARIMA is significantly faster than LSTM while the LSTM modelling time is increasing proportionally to the number of the training data points.
Publication Year: 2022
Publication Date: 2022-08-25
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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