A Comparison of ARIMA and LSTM in Forecasting Time Series

# A Comparison of ARIMA and LSTM in Forecasting Time Series > OpenAlex Metadata Hub · https://openalex.org/W2909877301 ## Bibliographic - **DOI:** 10.1109/icmla.2018.00227 - **Year:** 2018 - **Citations:** 1229 - **Open Access:** No (closed) - **License:** — - **Source:** https://doi.org/10.1109/icmla.2018.00227 ## Authors - Sima Siami‐Namini - Neda Tavakoli - Akbar Siami Namin ## Abstract 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. ## Keywords Autoregressive integrated moving average, Exponential smoothing, Univariate, Computer science, Time series, Artificial intelligence, Moving average, Series (stratigraphy), Machine learning, Deep learning, Autoregressive model, Algorithm, Econometrics, Mathematics, Multivariate statistics ## Concepts - Autoregressive integrated moving average - Exponential smoothing - Univariate - Computer science - Time series - Artificial intelligence - Moving average - Series (stratigraphy) - Machine learning - Deep learning - Autoregressive model - Algorithm - Econometrics - Mathematics - Multivariate statistics - Biology - Paleontology - Computer vision --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “A Comparison of ARIMA and LSTM in Forecasting Time Series” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): 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… Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.