Energy time series forecasting-analytical and empirical assessment of conventional and machine learning models

Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose of this paper is twofold, first it provides a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem. The first two problems are univariate TSF and the third problem is a multivariate TSF. Compared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the meantime, their computational requirements are notably greater than other contenders. Eventually, the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain.

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/397 ◽

2021 ◽

Author(s):

Qingyi Pan ◽

Wenbo Hu ◽

Ning Chen

Keyword(s):

Time Series ◽

Deep Learning ◽

Data Augmentation ◽

Multivariate Time Series ◽

Time Series Forecasting ◽

Experimental Results ◽

Learning Methods ◽

Forecasting Accuracy ◽

Sliding Windows ◽

Scheme Of Series

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.

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Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500

Journal of Computational Social Science ◽

10.1007/s42001-019-00057-5 ◽

2019 ◽

Vol 3 (1) ◽

pp. 103-133

Author(s):

Deniz Ersan ◽

Chifumi Nishioka ◽

Ansgar Scherp

Keyword(s):

Machine Learning ◽

Time Series ◽

Financial Time Series ◽

Time Series Forecasting ◽

Learning Methods ◽

Financial Time ◽

Machine Learning Methods ◽

Hourly Data

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Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms

Sustainability ◽

10.3390/su13020744 ◽

2021 ◽

Vol 13 (2) ◽

pp. 744

Author(s):

Elsa Chaerun Nisa ◽

Yean-Der Kuan

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Power Consumption ◽

Energy Demand ◽

Prediction Models ◽

Short Term Memory ◽

Time Series Forecasting ◽

Experimental Result ◽

Forecasting Model

Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R2), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R2, 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value.

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ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

Proceedings of the Shevchenko Scientific Society. Medical Sciences ◽

10.25040/ntsh2021.02.07 ◽

2021 ◽

Vol 65 (2) ◽

Author(s):

Oleksandr Dudin ◽

◽

Ozar Mintser ◽

Oksana Sulaieva ◽

◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Analysis ◽

Deep Learning ◽

Personalized Medicine ◽

Digital Pathology ◽

Automated Image Analysis ◽

Learning Methods ◽

Machine Learning Methods ◽

Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

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Modern technologies of deep learning for forecasting time series

Informacionno-technologicheskij vestnik ◽

10.21499/2409-1650-29-3-95-105 ◽

2021 ◽

pp. 95-105

Author(s):

А.И. Сотников

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Big Data ◽

Deep Learning ◽

Deep Neural Networks ◽

High Accuracy ◽

Time Series Forecasting ◽

Machine Learning Methods ◽

Modern Technologies

Прогнозирование временных рядов стало очень интенсивной областью исследований, число которых в последние годы даже увеличивается. Глубокие нейронные сети доказали свою эффективность и достигают высокой точности во многих областях применения. По этим причинам в настоящее время они являются одним из наиболее широко используемых методов машинного обучения для решения проблем, связанных с большими данными. Time series forecasting has become a very intensive area of research, the number of which has even increased in recent years. Deep neural networks have been proven to be effective and achieve high accuracy in many applications. For these reasons, they are currently one of the most widely used machine learning methods for solving big data problems.

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