A Data-Driven Selection of an Appropriate Seasonal Adjustment Approach

Webel, Karsten

doi:10.2139/ssrn.2797077

Cited by 3 publications

(2 citation statements)

References 35 publications

(38 reference statements)

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“…The former uses a model-based approach to identify the seasonal component; while the latter uses an empirical moving average approach to that end. According to Webel (2016), TRAMO tends to be better for shorter and moderate-length series. 23 We also tried trans-log and squared form models.…”

Section: Marginal Costmentioning

confidence: 99%

Ramsey pricing of aircraft landing fees: A case study of Iranian airports

Saremi

Fallahi

Pels

et al. 2021

Research in Transportation Economics

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Section: Marginal Costmentioning

confidence: 99%

Ramsey pricing of aircraft landing fees: A case study of Iranian airports

Saremi

Fallahi

Pels

et al. 2021

Research in Transportation Economics

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“…For finding stable seasonal patterns, the Kruskal-Wallis test is used [154]. The selected significance level is 0.05, i.e., smaller p-values suggest that there are indications of seasonality in the time series.…”

Section: Financial Distress Identification From the Drop In Employees...mentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

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Empty container movements arising from cargo seasonality: Turkish terminals

Basarici

Satır

2019

MABR

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Purpose -The purpose of this study is to reveal the magnitude of empty container movements (ECM) arising from cargo seasonality by means of long-term datasets of Turkish terminals. Trade imbalance is one of the well-known major reasons of ECM. Cargo seasonality apart from some other operational drivers and market effect, i.e. commercial decisions of the ship operators, is the major operational driver in Turkish terminals effecting ECM. Furthermore, this study highlights the significance of market effect, leading to take measures for more effective empty container operations in terms of decision makers leading the ship operators.Design/methodology/approach -Time series analysis of full container datasets was performed through X-13ARIMA-SEATS methodology, implementing seasonal adjustment.Findings -The results indicate that 17 of 112 time series in hand, based on a terminal/hinterland, container type and "in and out" foreign trade, exhibit cargo seasonality. Roughly, the amount of ECM originating from cargo seasonality in Turkish terminals represents 10 per cent of total ECM except trade imbalance in those terminals where seasonality is present. This reveals that ECM arising from market effect should not be underestimated.Research limitations/implications -Reefer container traffic could not be sorted from the datasets. Originality/value -This paper focuses on one of the major reasons of ECM, cargo seasonality. It brings a novel point of view and interpretations which were not suggested previously about ECM, motivating to overcome inefficiency in container operations.

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A Data-Driven Selection of an Appropriate Seasonal Adjustment Approach

Cited by 3 publications

References 35 publications

Ramsey pricing of aircraft landing fees: A case study of Iranian airports

Ramsey pricing of aircraft landing fees: A case study of Iranian airports

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Empty container movements arising from cargo seasonality: Turkish terminals

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