A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

Liu, Gedi; Zhong, Keyang; Li, Huilin; Chen, Tao; Wang, Yan

doi:10.1016/j.inpa.2022.10.005

Cited by 10 publications

(4 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An analysis of the literature has shown that, compared with traditional models, deep neural networks can enhance data structure mining and overall information simulation capabilities through innovative and efficient structures. This means that it is also possible to extend the range of environmental parameter selection for agricultural facilities and to achieve environmental prediction end-to-end optimisation through an intelligent time series model based on deep neural networks [74].…”

Section: Analysis Of Time Series and Their Neural Network Representationmentioning

confidence: 99%

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

et al. 2023

View full text Add to dashboard Cite

Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.

show abstract

Section: Analysis Of Time Series and Their Neural Network Representationmentioning

confidence: 99%

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Therefore, finding a solution to the intricacies of non-stationary data is vital. Scholars across various fields have explored it, particularly in time-series modelling [12,13] and quantitative finance [14]. In these areas, the focus has often been on adapting models to handle the unpredictable nature of data over time, thereby enhancing predictive accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Non-stationary Transformer Architecture: A Versatile Framework for Recommendation Systems

Liu,

Li,

Payne

et al. 2024

Preprint

View full text Add to dashboard Cite

Recommendation systems are crucial in navigating the vast digital market. However, user data’s dynamic and non-stationary nature often hinders their efficacy. Traditional models struggle to adapt to the evolving preferences and behaviours inherent in user interaction data, posing a significant challenge for accurate prediction and personalisation. Addressing this, we propose a novel theoretical framework, the Non-stationary Transformer, designed to capture and leverage the temporal dynamics within data effectively. This approach enhances the traditional transformer architecture by introducing mechanisms accounting for non-stationary elements, offering a robust and adaptable solution for recommendation systems. Our experimental analysis, encompassing deep learning and reinforcement learning paradigms, demonstrates the framework’s superiority over benchmark models. The empirical results confirm the efficacy of our proposed framework, which not only provides significant performance enhancements, approximately 8% in Logloss reduction and up to 2% increase in F1 score but also underscores its potential applicability across accumulative reward scenarios. These findings advocate adopting Non-stationary Transformer models to tackle the complexities of today’s recommendation tasks.

show abstract

“…The main reason for this is the fundamental difference in the spaces in which members of fundamentally different populations live and interact. In this context, the mathematical apparatus of time series analysis is suitable for describing the development of cyber epidemics [34][35][36][37][38] . However, the disadvantage of this approach is the strength of compartmental models-time series analysis models do not take into account the specifics of the development of the cyber epidemic.Machine learning is a powerful tool for determining the relationship between input and output data for processes that are difficult to analyze analytically.…”

mentioning

confidence: 99%

“…Noise filtering allows us to refine the prediction and can be performed both during the pre-processing of the raw data and directly in the body of the prediction algorithm. One such approach is wavelet decomposition 35,36 , in which a short time series is represented by wavelet functions. This approach is usually used in conjunction with other models.…”

mentioning

confidence: 99%

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Kovtun,

Grochla,

Kharchenko

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

show abstract

A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

Cited by 10 publications

References 55 publications

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Non-stationary Transformer Architecture: A Versatile Framework for Recommendation Systems

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Contact Info

Product

Resources

About