A Comparison of ARIMA and LSTM in Forecasting Time Series

By Sima Siami‐Namini, Neda Tavakoli, Akbar Siami Namin. International Conference on Machine Learning and Applications, 2018

1,065 citations recorded by the source corpus.

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly development of more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to analyze and forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as "Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM was between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as "epoch" in deep learning, had no effect on the performance of the trained forecast model and it exhibited a truly random behavior.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e35338965b359217c3c8360f4c5d3e79beb5f9bc

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In Electrical and Electronic Engineering

1. Sima Siami‐Namini
2. Akbar Siami Namin
3. Neda Tavakoli

In Management Science and Operations Research

1. Sima Siami‐Namini
2. Akbar Siami Namin
3. Neda Tavakoli

Citations in the corpus

• A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks (2023, neutral): “The general form of an ARIMA model is written as ARIMA(p, d, q), including the integration term that guarantees the stationarity of the time series [13], and it can be expressed as: where ∇ d is a differential factor, introducing a difference of order d , aiming to remove the nonstationarity of the…”
• The Performance of LSTM and BiLSTM in Forecasting Time Series (2019, methodology): “The authors partially reused the previously collected data [20], in which daily, weekly, and monthly time series of some stock data for the period of Jan 1985 to Aug 2018 were extracted from the Yahoo finance Website 1 .”
• Applications of deep learning in stock market prediction: recent progress (2020, neutral): “D CNN DNN, SVM Deng et al. (2019) KDTCN ARIMA, LSTM, CNN, TCN Sezer & Ozbayoglu (2019) CNN Buy&Hold Sim et al. (2019) CNN ANN, SVM Recurrent Neural Network Type Huynh et al. (2017) BGRU LSTM, GRU, DNN (Ding et al., 2014), DNN (Peng & Jiang, 2016) Li & Tam (2017) WT+LSTM LSTM Li et al. (2017a) LSTM S”