Stock Price Prediction Using the ARIMA Model

By Adebiyi Ariyo Ariyo, A. Adewumi, C. Ayo. 2014

1,016 citations recorded by the source corpus.

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

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Citations in the corpus

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting (2023, background): “Time series forecasting has been a critical role in a variety of real-world industries, such as climate condition estimation [1, 2], traffic state prediction [3, 4], economic analysis [5, 6], etc.”
A Comparison of ARIMA and LSTM in Forecasting Time Series (2018, background): “Even though ARIMA models are very prevalent in modeling economical and financial time series [1], [2], [14], they have some major limitations [6].”
Restricted Boltzmann machine based stock market trend prediction (2017, neutral): “smoothing, and ARIMA are highlighted applications [16].”
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (2020, methodology): “We have selected 5 time-series forecasting methods as comparison, including ARIMA (Ariyo, Adewumi, and Ayo 2014), Prophet (Taylor and Letham 2018), LSTMa (Bahdanau, Cho, and Bengio 2015) and LSTnet (Lai et al. 2018) and DeepAR (Flunkert, Salinas, and Gasthaus 2017).”
Are Transformers Effective for Time Series Forecasting? (2022, methodology): “…prediction accuracy improvements over traditional methods, in their experiments, all the compared (non-Transformer) baselines perform autoregressive or iterated multi-step (IMS) forecasting [1,2,22,24], which are known to suffer from signiﬁcant error accumulation effects for the LTSF problem.”
The Performance of LSTM and BiLSTM in Forecasting Time Series (2019, methodology): “These techniques have been used for a long time in modeling time series problems [1], [2], [17].”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, neutral): “autoregressive integrated moving average model (ARIMA) [5].”
Neural Networks Fail to Learn Periodic Functions and How to Fix It (2020, methodology): “Qualitatively, we focus on making comparison with ARIMA, a traditional and standard method in economics and stock price prediction [29, 2, 41].”
TS2Vec: Towards Universal Representation of Time Series (2021, methodology): “Time Series Forecasting Deep learning methods including RNNs (Salinas et al. 2020; Wen et al. 2017; Oreshkin et al. 2019), CNNs (Bai, Kolter, and Koltun 2018; Wan et al. 2019), GNNs (Cao et al. 2020) and Transformers (Li et al. 2019; Zhou et al. 2021), have been widely applied to time series forecas”
Transformers in Time-Series Analysis: A Tutorial (2022, methodology): “The model performed better than state-of-the-art (SOTA), including Autoregressive Integrated Moving Average (ARIMA) [85], Prophet [86], LSTMa [87], LSTnet [88], and DeepAR [89].”
Short-term stock market price trend prediction using a comprehensive deep learning system (2020, methodology): “Ayo [2] Close price of stock data from New York Stock Exchange (NYSE) ARIMA 0.”
A CNN-LSTM-Based Model to Forecast Stock Prices (2020, background): “Based on statistics and probability theory, some scholars use time series linear forecasting model to predict the short-term stock price with a large number of long-term data, such as vector autoregression (VAR) [7], Bayesian vector autore-gression (BVAR) model [8], autoregressive integrated moving ”