A Deep Gaussian Process for Forecasting Crop Yield and Time Series Analysis of Precipitation Based in Munshiganj, Bangladesh

Mahdi, Mostafa Didar; Mrittika, Nusrat Jahan; Shams, Maleeha; Chowdhury, Labib; Siddique, Shahnewaz

doi:10.1109/igarss39084.2020.9323423

Cited by 3 publications

(1 citation statement)

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“…Year Application [135] 2017 Crop Yield forecasting [136] 2020 Crop Yield forecasting [137] 2022 Electricity load [138] 2023 Car-hailing demand [139] 2023 Ozone concentration forecasting…”

Section: Refmentioning

confidence: 99%

Deep Learning for Time Series Forecasting: Advances and Open Problems

Casolaro,

Capone,

Iannuzzo

et al. 2023

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A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline recent advances and open problems, and also pay attention to benchmark data sets. Moreover, the work presents a clear distinction between deep learning architectures that are suitable for short-term and long-term forecasting. With respect to existing literature, the major advantage of the work consists in describing the most recent architectures for time series forecasting, such as Graph Neural Networks, Deep Gaussian Processes, Generative Adversarial Networks, Diffusion Models, and Transformers.

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“…Year Application [135] 2017 Crop Yield forecasting [136] 2020 Crop Yield forecasting [137] 2022 Electricity load [138] 2023 Car-hailing demand [139] 2023 Ozone concentration forecasting…”

Section: Refmentioning

confidence: 99%