Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns

Naim, Iram; Mahara, Tripti; Idrisi, Ashraf Rahman

doi:10.1016/j.procs.2018.05.136

Cited by 30 publications

(20 citation statements)

References 30 publications

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“…In this work, we use three different time series models: Trigonometric, Box-Cox transform, ARMA errors, Trend, and Seasonal components model (TBATS) [75], Prophet [76], and Long Short-Term Memory (LSTM) [77].…”

Section: Modelsmentioning

confidence: 99%

“…TBATS is one of the innovations in the state space modeling frameworks, designed for handling complex time dependent data series [75]. It uses Fourier terms combined with exponential smoothing and Box-Cox transformation in automated fashion.…”

Section: Modelsmentioning

confidence: 99%

“…It uses Fourier terms combined with exponential smoothing and Box-Cox transformation in automated fashion. Due to the trigonometric functions in TBATS, it provides the capabilities to model non-integer seasonal frequencies [75]. Some of the main advantages of TBATS are: (i) it can handle non-linearity present in features using Box-Cox transformation (ii) it detects any auto-correlation present in the residuals using ARMA and (iii) and it is capable of accommodating both nested and non-nested seasonal components in time series [75].…”

Section: Modelsmentioning

confidence: 99%

“…Due to the trigonometric functions in TBATS, it provides the capabilities to model non-integer seasonal frequencies [75]. Some of the main advantages of TBATS are: (i) it can handle non-linearity present in features using Box-Cox transformation (ii) it detects any auto-correlation present in the residuals using ARMA and (iii) and it is capable of accommodating both nested and non-nested seasonal components in time series [75]. Its applications can be seen in some of recent studies [78], such as modelling electricity load demand [79] or seasonality of pathogens [78].…”

Section: Modelsmentioning

confidence: 99%

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Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

et al. 2021

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Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

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Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

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Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

et al. 2021

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“…Time series forecasting is a process that predicts future events based on a historical trend with data that has an active role in business decision making in several domains. Moreover, researchers take into account traditional time series techniques such as ARIMA and SARIMA, while also considering advanced techniques including BATS and TBATS [28]. The time series forecasting is stated to be fundamental for many applications, including business, stock market, and exchange.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

et al. 2020

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Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results.

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Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review

Lima

Silva

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Assessing COVID-19 and Other Pandemics and Epidemics Using Computational Modelling and Data Analysis

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Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns

Cited by 30 publications

References 30 publications

Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review

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