Machine Learning Strategies for Time Series Forecasting

By Gianluca Bontempi, Souhaib Ben Taieb, Yann-Aël Le Borgne. 2012

545 citations recorded by the source corpus.

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

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Financial Time Series Prediction Using Deep Learning (2017, methodology): “Due to the inherent uncertainty and non-analytic structure of ﬁnancial markets [5], the task proved to be challenging, where classical linear statistical methods such as the ARIMA model, and statistical machine learning (ML) models have been widely applied [6, 7].”
Data Poisoning Attacks against Autoregressive Models (2016, background): “See (Bontempi, Taieb, and Le Borgne 2013) for further details and a review of machine learning methods for forecasting.”
COVID-19 Future Forecasting Using Supervised Machine Learning Models (2020, unknown): “One of the most signiﬁcant areas of ML is forecasting [2], numerous standard ML algorithms have been used in this area to guide the future course of actions needed in many application areas including weather forecasting, disease forecasting, stock market forecasting as well as disease prognosis.”
Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs (2017, background): “A considerable number of stochastic [1] and machine learning based [2] approaches have been proposed for this problem.”
A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “Researchers can adopt appropriate strategies to solve such problems in forecating field, such as Recursive, DirRec, and Multiple Output Strategies [168].”
Forecasting stock prices with long-short term memory neural network based on attention mechanism (2020, background): “Due to the nonstationary, nonlinear, high-noise characteristics of financial time series,[7] traditional statistical models have difficulty predicting them with high precision.”
Quantum computing for finance: Overview and prospects (2018, background): “This process, known as regression, is particularly useful when attempting an interpolation, and is consequently a core tool for economic forecasting [66].”