The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction

Dagum, Estela Bee; Bianconcini, Silvia

doi:10.1007/978-3-319-31822-6_11

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…Decomposing the frequency response function, Koopmans (1974) shows that the one-sided simple moving average transformation has a non-zero phase function in contrast with the zero phase function of the symmetric moving average transformation, which means a delay of half the length of the moving average compared to the original untransformed time series. The same conclusion can be found in many other publications, among others, for example, in Oppenhaim and Schafer (1989), Ladiray and Quenneville (2001), Quenneville and Findley (2012), or Dagum and Bianconcini (2016).…”

Section: Introductionsupporting

confidence: 83%

“…With growing frequencies, it decreases to zero at the basic seasonal frequency for the monthly time series f = 1/12 and its harmonics f = k/12, k = 2, 3, 4, 5, 6. This means that the moving average retains the trend and cyclical components in the time series and removes the seasonal component; see, for example, Dagum and Bianconcini (2016).…”

Section: Moving Average Frequency Responsementioning

confidence: 99%

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The problem of annual inflation rate indicator

Arlt

2021

Int. J Fin Econ

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In economic practice, the annual inflation rate is commonly used. As a onesided moving average of the annualized inflation rate, it is approximately 6 months behind the annualized and monthly inflation rates and the CPI. The annual rate of inflation is, in fact, a smoothing transformation that removes the seasonal component from the annualized inflation rate, assuming the presence of all seasonal unit roots. In reality, however, the CPIs, and thus the annualized inflation rates, do not contain most of seasonal unit roots, resulting in spurious cycles of annual inflation rates, which pose difficulties in assessing and interpreting their development dynamics. Their practical applications in econometric analyses are limited because the unit root tests too often do not reject the zero hypothesis of the presence of unit roots when annual inflation rates are actually stationary. Parameter estimates of the annual inflation rate models are biased and inaccurate, allowing only incorrect short-term forecasts.The above facts make use of the annual inflation rate indicator in real economic situations questionable.

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Section: Introductionsupporting

confidence: 83%

Section: Moving Average Frequency Responsementioning

confidence: 99%

The problem of annual inflation rate indicator

Arlt

2021

Int. J Fin Econ

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“…This method uses the Box-Jenkins (ARIMA) model to forecast the series. For details on this method, see Dagum and Bianconcini (2016). Another common decomposition method is the empirical mode decomposition (EMD) (Huang et al, 1998), which reduces any given time series into a collection of intrinsic mode functions (IMFs).…”

Section: Time Series Decompositionmentioning

confidence: 99%

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

Lara‐Alvarez,

Flores,

Rodriguez‐Rangel

et al. 2024

WIREs Data Min & Knowl

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Satellite image time‐series are time series produced from remote sensing images; they generally correspond to features or indicators extracted from those images. With the increasing availability of remote sensing images and new methodologies to process such data, image time‐series methods have been used extensively for assessing temporal pattern detection, monitoring, classification, object detection, and feature estimation. Since the study of time series is broad, this article focuses on analyzing articles related to forecasting the value of one or more attributes of the image time‐series. The image time series forecasting (ITSF) problem appears in different disciplines; most focus on improving the quality of life by harnessing natural resources for sustainable development and minimizing the lethality of dangerous natural phenomena. Scientists tackle these problems using different tools or methods depending on the application. This review analyzes the field's leading, most recent contributions, grouping them by application area and solution methods. Our findings indicate that artificial neural networks, regression trees, support vector regression, and cellular automata are the most common methods for ITSF. Application areas address this problem as renewable energy, agriculture, and land‐use change. This study retrieved and analyzed relevant information about the recent activity of image time series forecasting, generating a reproducible list of the most pertinent articles in the field published from 2009 to 2021. To the author's best knowledge, this is the first review presenting and analyzing a reproducible list of the most relevant state‐of‐the‐art articles focusing on the applications, techniques, and research trends for ITSF.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Machine Learning Technologies > Prediction

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“…a Gaussian posterior is used to select the next evaluation point. To determine x t , an auxiliary optimization program is used to maximize the acquisition function across all X (usually, it approximates the maximization of that point) (Dagum and Bianconcini, 2016;Xiong et al, 2018;Yin et al, 2020).…”

Section: Bayesian Optimization Algorithmmentioning

confidence: 99%

Ensemble Framework for Multistep Carbon Emission Prediction: An Improved Bi-LSTM Model Based on the Bayesian Optimization Algorithm and Two-Stage Decomposition

Song,

Bai,

Wang

et al. 2024

Preprint

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Effective prediction of carbon dioxide emissions is crucial for the real-time monitoring of carbon emission dynamics and the formulation of emission reduction policies. For the accurate and stable prediction of carbon emission data, multiple challenges must be addressed, mainly including nonlinearity, nonstationarity, and dynamic uncertainty. To further improve the accuracy of carbon emission prediction, an ensemble framework for daily CO₂ emission forecasting based on data decomposition-reconstruction is proposed. First, the original data are decomposed into trend, seasonal, and residual components using seasonal trend decomposition via the Loess method. For the trend and seasonal components, the Bayesian optimization algorithm (BOA) is applied to optimize a bidirectional long short-term memory (Bi-LSTM) neural network for further prediction. The Bi-LSTM adopts a novel LeakyReLU activation function to effectively extract data features, thereby enhancing the model's expressive power. Meanwhile, the Nadam optimizer is employed, which combines the adaptive learning rate mechanism of the Adam algorithm with the Nesterov momentum to update weights and biases, enabling efficient model training, accelerating convergence, and improving model performance. Then, the residual component is decomposed again using gray wolf optimizer (GWO)-optimized variational mode decomposition (VMD), VMD incorporates a quadratic penalty function and Lagrange multipliers to derive the input series. and the decomposed subsequences are predicted using gated recurrent unit (GRU) networks. Finally, the predictions of each subsequence are stacked and reconstructed to obtain the final forecast value, creating an ensemble framework referred to as STL-BOA-Bi-LSTM-GWO-VMD-GRU. The proposed ensemble framework is tested on real data from seven different regions. In both single-step and multistep prediction processes, the proposed ensemble framework has higher prediction accuracy and stability than other models, demonstrating significant advantages.

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The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction

Cited by 6 publications

References 9 publications

The problem of annual inflation rate indicator

The problem of annual inflation rate indicator

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

Ensemble Framework for Multistep Carbon Emission Prediction: An Improved Bi-LSTM Model Based on the Bayesian Optimization Algorithm and Two-Stage Decomposition

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