Assessment of dynamic line rating forecasting methods

Lawal, Olatunji Ahmed; Teh, Jiashen

doi:10.1016/j.epsr.2022.108807

Cited by 21 publications

(2 citation statements)

References 39 publications

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“…The seasonal component (P, D, Q) [s] of the model, represented as (0,0,2) [12] , shows that the data has a seasonal component repeating every 12 periods (likely indicating monthly data with yearly seasonality). The (0,0,2) parameters suggest that no autoregressive or differencing terms are used for the seasonal component, but two past seasonal forecast errors are incorporated in the model [7]. Seasonality patterns within the model were then explored.…”

Section: Resultsmentioning

confidence: 99%

Forecasting USA Unemployment Rate Base on ARIMA Model

Zhang

2023

AEMPS

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This paper presents a detailed analysis of unemployment rate forecasting, a critical subject for various stakeholders including policymakers, businesses, and individuals. Amid significant economic events such as the global financial crisis and COVID-19 pandemic, the need for precise unemployment forecasts has become crucial. The research utilizes an Autoregressive Integrated Moving Average (ARIMA) model to analyze US unemployment rate data from 2000 to 2023, sourced from the Federal Reserve Economic Data (FRED). The paper identifies seasonality patterns, executes appropriate data transformations, and incorporates the Box-Jenkins methodology for ARIMA model identification. The findings reveal the model's resilience, demonstrating accurate forecasts despite significant disruptions. These insights offer valuable contributions in understanding labor market dynamics, facilitating informed decision-making and strategic planning. The paper highlights the robustness of ARIMA models, and their potential to adapt to rapid changes in the economic landscape, thereby proving invaluable in forecasting unemployment rates.

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Section: Resultsmentioning

confidence: 99%

Forecasting USA Unemployment Rate Base on ARIMA Model

Zhang

2023

AEMPS

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“…In [36] and [37], the authors have shown the effect of DTR and storage devices on improving the reliability of electrical networks. It has been shown in [38] that deep learning is an effective method for predicting DTR. [39][40][41] show the significant impact of DTR and information and communication technologies on improving the reliability of cyber-physical systems.…”

Section: Literature Reviewmentioning

confidence: 99%