Review of automated time series forecasting pipelines

Meisenbacher, Stefan; Turowski, Marian; Phipps, Kaleb; Rätz, Martin; Müller, Dirk; Hagenmeyer, Veit; Mikut, Ralf

doi:10.1002/widm.1475

Cited by 33 publications

(10 citation statements)

References 134 publications

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“…Following data visualization, transformations can be applied to improve feature interpretability (for example, removing high-frequency noise, introducing lag features, feature derivatives, etc. [49]) or conform with the assumptions of machine learning models, such as linear regression (linearity, residual independence, homoscedasticity, and residual normality). For example, in order for a researcher to use linear regression models, a linear relationship between the independent variable and the target variable is required, something not necessary if a neural network architecture was selected.…”

Section: Scaling Methods Scaled Feature Scaling Effect ML Algorithm/m...mentioning

confidence: 99%

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Kampezidou,

Tikayat Ray,

Bhat

et al. 2024

Eng

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This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.

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Section: Scaling Methods Scaled Feature Scaling Effect ML Algorithm/m...mentioning

confidence: 99%

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Kampezidou,

Tikayat Ray,

Bhat

et al. 2024

Eng

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“…Price forecasting of financial time series complies with the rule of point forecasting of general time series. Given a time series

y

, whose time steps are given as

k \in [1, K]

, a time series forecasting model

f (\cdot)

estimates the future value

\overset{truê}{y}

at a forecast horizon

H \geq 1

, utilizing the historical values (from time steps

k

k - H_{1}

, with

H_{1}

being the time lags) of the desired time series and several exogenous time series, which are denoted as

y [k], \dots, y [k - H_{1}]

and

{boldu}^{T} [k], \dots, {boldu}^{T} [k - H_{1}]

, respectively (González Ordiano et al, 2018; Meisenbacher et al, 2022). This functional relationship is defined as follows:

\overset{truê}{y} [k + H] = f (y [k], \dots, y [k - H_{1}], {boldu}^{T} [k], \dots, {boldu}^{T} [k - H_{1}], boldθ); k > H_{1},

where the vector

θ

denotes the model parameters.…”

Section: Forecasting Background Informationmentioning

confidence: 99%

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Zhang,

Sjarif,

Ibrahim

2023

WIREs Data Min & Knowl

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Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

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“…On the other hand, relative books in pipeline development focus more on coding using a specific language and often lack important concept explanations, such as the bias-variance decomposition [45][46][47]. Other review papers focus entirely on the automation of a very specific machine learning area, such as time series forecasting automation [48], and lack the explanation of basic AI principles and workflow sub-module operation, interconnection and development for the education and guidance of a non-AI researcher, seeking to understand and complete their manual workflow development before automating. Last, there are some machine learning workflow automation papers [41,49], that focus on a very specific application, i.e.…”

Section: Of 36mentioning

confidence: 99%

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Kampezidou,

Tikayat Ray,

Bhat

et al. 2023

Preprint

View full text Add to dashboard Cite

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

show abstract

Review of automated time series forecasting pipelines

Cited by 33 publications

References 134 publications

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

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