Time Series Analysis Techniques Research Articles

Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies’ Staffing Needs and Order Volume

Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet their objectives and ensure sustained growth. The study aims to build a model that optimizes the prediction of order volume during specific time periods and determines the staffing requirements for the company. The prediction of order volume in logistics companies involves analyzing trend and seasonality components in the data. Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) are well-established and effective in capturing these patterns, providing interpretable results. Deep-learning algorithms require more data for training, which may be limited in certain logistics scenarios. In such cases, traditional models like SARIMAX, ARIMA, and AR can still deliver reliable predictions with fewer data points. Deep-learning models like LSTM can capture complex patterns but lack interpretability, which is crucial in the logistics industry. Balancing performance and practicality, our study combined SARIMAX, ARIMA, AR, and Long Short-Term Memory (LSTM) models to provide a comprehensive analysis and insights into predicting order volume in logistics companies. A real dataset from an international shipping company, consisting of the number of orders during specific time periods, was used to generate a comprehensive time-series dataset. Additionally, new features such as holidays, off days, and sales seasons were incorporated into the dataset to assess their impact on order forecasting and workforce demands. The paper compares the performance of the four different time-series analysis methods in predicting order trends for three countries: United Arab Emirates (UAE), Kingdom of Saudi Arabia (KSA), and Kuwait (KWT), as well as across all countries. By analyzing the data and applying the SARIMAX, ARIMA, LSTM, and AR models to predict future order volume and trends, it was found that the SARIMAX model outperformed the other methods. The SARIMAX model demonstrated superior accuracy in predicting order volumes and trends in the UAE (MAPE: 0.097, RMSE: 0.134), KSA (MAPE: 0.158, RMSE: 0.199), and KWT (MAPE: 0.137, RMSE: 0.215).

What Drives Business Cycles in Egypt? An Analysis of Coincident and Leading Indicators

Abstract The paper proposes a chronology for the Egyptian economy by detecting phases of expansions and recessions during the period (2002–2019). It examines the cyclical behavior of variables that are considered potentially useful in measuring or predicting aggregate economic activity. To do so, we combine the National Bureau of Economic Research (NBER) approach together with time series analysis techniques to select the variables best suitable to play the role of coincident and leading indicators. This methodology is found to be the most appropriate when there is no well-established reference chronology as a benchmark for the cyclical analysis, which is the case of Egypt. As a result, two composite indexes are constructed: 1) the composite index of coincident economic indicators (CEI), which can be considered an adequate measure for the Egyptian business cycle; 2) the composite index of leading economic indicators (LEI) that shows good performance in anticipating aggregate economic activity in Egypt. Moreover, the empirical results indicate that total employment, consumption, and investment move coincidently with the reference cycle, while exchange rate and interest rate variables lead the reference cycle and have strong predictive power for economic activity in Egypt.

Digital Transformation in Public Sector: Case Study on Licensing Services to Start a Business in Pekalongan City

This study had two formulated objectives. First, this study wanted to evaluate the achievements of the digital transformation process in licensing services to start a business in Pekalongan City, Central Java, Indonesia. Second, this study also expected to identify the factors that influenced the achievement of the digital transformation process in licensing services to start a business in Pekalongan City. This study used qualitative approach to answer the problem formulation through a case study. Data and information were analyzed using chronological time series analysis techniques. The informants of this research were selected through the snowball method. The results of data and information analysis showed that Pekalongan City was at the e-government stage. Three reasons that justified that the achievement was not at the digital government stage. First, users or citizens had not voiced demand in the digital transformation process. Second, the transformation of internal work processes had not yet taken place. Third, there had not been a shared database sharing process between related agencies either within the Pekalongan City government environment or with central government agencies. The analysis also showed that five factors that influenced the process of digital transformation in licensing services to start a business in Pekalongan City, those were: strategic factors, digital cultural factors, leadership factors, human resource capacity factors, and the role factor of central government organizations.

Forecasting road accidental deaths in India: an explicit comparison between ARIMA and exponential smoothing method

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022–2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India.

Characterising the variability in ocean data using fractal and correlation dimension analysis

Understanding the variability of ocean data is essential for predicting its behaviour in time domain. This study aims to examine the usability of several nonlinear time series analysis techniques for diagnosing the complexity of the underlying dynamics of various ocean data. For such purpose, one-year data from an offshore platform and two-year data from a weather buoy were collected and analysed. The results indicate that all the ocean data considered here are chaotic in nature, reflected by the unfolded structure in the reconstructed phase space. In general, the surface currents speed possess the largest fractal dimension (or lowest Hurst exponent) and correlation dimension, while the significant wave height has the lowest values. This implies that the dynamics associated with surface currents speed tend to be the most complex, which can lead to more random and unpredictable temporal behaviour. The fractal analysis shows that the fluctuation of these ocean data are mostly persistent, and the dependence of fractal properties on window length appears to vary amongst different ocean data. Overall, the results derived from various analysis techniques are consistent, which confirms the potential of using chaotic time series analysis technique for characterizing ocean data variability.

Time Series Prediction of Lung Cancer Death Rates on the Basis of SEER Data.

The purpose of this study was to apply different time series analytical techniques to SEER US lung cancer death rate data to develop a best fit model. Three models for yearly time series predictions were built: autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES), and Holt's double expansional smoothing (HDES) models. The three models were built using Python 3.9, on the basis of Anaconda 2022.10. This study used SEER data from 1975 to 2018 and included 545,486 patients with lung cancer. The best parameters for ARIMA are ARIMA (p, d, q) = (0, 2, 2). In addition, the best parameter for SES was α = .995, whereas the best parameters for HDES were α = .4 and β = .9. The HDES was the model that best fit the lung cancer death rate data, with a root mean square error (RMSE) of 132.91. Including monthly diagnoses, death rates, and years in SEER data increases the number of observations for training and test sets, enhancing the performance of time series models. The reliability of the RMSE was based on the mean lung cancer mortality rate. Owing to the high mean lung cancer death rate of 8,405 patients per year, it is acceptable for reliable models to have large RMSEs.

A Review of Road Traffic Accident Prediction Methods

With the continuous development of urban traffic and the acceleration of urbanization, traffic accidents have become an important issue for urban safety and social stability. In order to prevent and reduce the occurrence of traffic accidents, traffic accident prediction technology has gradually become a hot spot for research. This paper analyzes road traffic accident prediction techniques from articles included in relevant English journals and provides a detailed introduction to the road traffic accident prediction techniques that are already in existence. This paper introduces the current status of research on traffic accident prediction techniques, including traditional statistical analysis methods, machine learning methods, neural network methods, time series analysis methods and techniques based on spatio-temporal data mining, and analyses the advantages and disadvantages of each road traffic accident prediction method. These methods are able to analyse the influencing factors of traffic accidents, build prediction models, improve prediction accuracy and provide strong support for road traffic accident prevention effects for urban traffic safety. Finally, the main difficulties faced in road traffic accident prediction and the future development trend of road traffic accident prediction is discussed. The work done in this paper can provide necessary theoretical support for relevant researchers and save the time needed for literature review.

Temporary disruption in language processing reflected as multiscale temporal discoordination in a recurrent network

By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.

Temporary disruption in language processing reflected as multiscale temporal discoordination in a recurrent network

By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.

Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications

Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO4) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO4 compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt’s Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I–V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.

Handling the risk dimensions of wind energy generation

In this paper, we explore two strategies for reducing the cash flow uncertainty of wind energy producers ascribed to variable weather conditions. The first strategy is based on the idea of aggregating the output of geographically-dispersed generating units. The second strategy employs financial instruments that compensate producers for unanticipated declines in the power delivery. Using a blend of advanced weather modeling, time series analysis, simulation and optimization techniques, we empirically access the ability of the two risk management approaches to control volumetric risk in Spain. With the aid of factor analysis techniques, we proceed with the decomposition of the remaining risk levels and assess the exposure of each strategy’s revenue to systematic risk factors. Motivated by the results of this analysis, we propose new financial contracts and mixed-style strategies that are better suited to the risk position of each market player.

Mapping tourism strategy patterns on tourism area life cycle

PurposeTourist destinations are constantly changing products, evolving as per the controls exerted by the stakeholders. The study aims to map the pattern of tourism development and identify the strategies formed at the destination over a seven-decade period for a state as a unit of analysis.Design/methodology/approachThe paper evaluates tourism development through the tourism area life cycle (TALC) model and uses Mintzberg's strategy analysis process to identify strategies. The study involves time series analysis, pattern matching and explanation-building techniques. The TALC is plotted for the number of tourist arrivals from 1947 to 2019, and strategies are mapped for each stage.FindingsThe TALC shows a cycle-recycle pattern of tourism development. The research revealed several strategies at different stages. Both the central and state governments and entrepreneurs, distinctively and in conjunction, have formed strategies. The pattern shows the period of piecemeal and global strategic changes contributing to tourism development.Research limitations/implicationsThe research unearths the strategies that drive the development curves of TALC, emphasising the integration of TALC with other theories. The research also assesses the strategy formed in the pre-tourism stage.Practical implicationsThe research brings to light the use of TALC as a strategic road-mapping tool. In addition, the study emphasises the significance of global and piecemeal strategic periods and stakeholder's regulatory and operational roles.Originality/valueThe research uses a unique methodology that maps the strategies, periods of strategic changes and incremental strategies for each stage of TALC, along with identifying the stakeholders.

How the consequences of the COVID-19 pandemic affected housing sector? Empirical evidence from Turkey

PurposeThis study aims to analyze the relationship between the consequences of the pandemic and the housing sector with econometric tests that allow for structural breaks.Design/methodology/approachStudy data were collected weekly between March 9, 2020, and February 4, 2022, and analyzed for Turkey. In the model of the study, housing loans were used as a housing market indicator, and the number of new deaths and new cases were used as data related to the pandemic. The exchange rate, which affects the use of housing loans, was added to the model as a control variable. This study was analyzed to examine the relationship between the pandemic and the housing sector, time series analysis techniques that allow structural breaks were used.FindingsBased on the result of the analyses, it was concluded that there is a long-run relationship between the pandemic stages and housing markets along with structural breaks. As a result of the time-varying causality test developed to determine the causality relationship between the variables and its direction, a bidirectional causality relationship was identified between all variables at certain dates.Research limitations/implicationsStudy data were collected weekly between March 9, 2020, and February 4, 2022, and analyzed in the case of Turkey.Practical implicationsBased on results of the study, it is recommended that policy makers and market actors take into account extraordinary situations such as pandemics and create a budget allocation that is always ready to use for this purpose.Originality/valueThe empirical examination of the relationship between the pandemic and the housing sector in Turkey provides originality to this study in terms of its topic, sample, methodology, contribution to the literature and potential policy recommendations.

Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data

ABSTRACT Understanding the temporal characteristics of data from low-frequency radio telescopes is of importance in devising suitable calibration strategies. Application of time-series analysis techniques to data from radio telescopes can reveal a wealth of information that can aid in calibration. In this paper, we investigate singular spectrum analysis (SSA) as an analysis tool for radio data. We show the intimate connection between SSA and Fourier techniques. We develop the relevant mathematics starting with an idealized periodic dataset and proceeding to include various non-ideal behaviours. We propose a novel technique to obtain long-term gain changes in data, leveraging the periodicity arising from sky drift through the antenna beams. We also simulate several plausible scenarios and apply the techniques to a 30-day time series data collected during 2021 June from SITARA – a short-spacing two element interferometer for global 21-cm detection. Applying the techniques to real data, we find that the first reconstructed component – the trend – has a strong anti-correlation with the local temperature suggesting temperature fluctuations as the most likely origin for the observed variations in the data. We also study the limitations of the calibration in the presence of diurnal gain variations and find that such variations are the likely impediment to calibrating SITARA data with SSA.

Large Femoral Heads and Select Dual-Mobility Bearings Are Associated With Reduced Instability in Contemporary Posterior Approach Total Hip Arthroplasty

BackgroundPostoperative dislocation is considered the main drawback of posterior approach total hip arthroplasty (THA). Thinner highly cross-linked polyethylene and dual-mobility bearings allow maximizing femoral head diameter per a given cup size. This study evaluated dislocation rates as large femoral head bearings were introduced into a practice over an 11-year period. MethodsA total of 1,511 consecutive primary THAs were retrospectively reviewed. Demographics, implant sizes, femoral head-acetabular cup ratio, and dislocation status were collected from the electronic medical record. Data were evaluated using time series analysis techniques as larger femoral heads, thinner polyethylene liners, and dual-mobility bearings were introduced. The cohort was 57% women with mean age and body mass index of 62 years (range, 13 to 93) and 31 kg/m2 (range, 13 to 54), respectively. ResultsThe overall dislocation rate was 0.98%. Use of femoral head sizes ≥ 40 millimeters increased from 4% in the years 2010 to 2016 to 51% in the years 2017 to 2021, correlating with a 50% reduction in dislocation rate from 1.4% to 0.7% (P = .279). Also, no dislocations occurred in patients who had dual-mobility bearings or ≥ 40-millimeter femoral heads (P = .007). Twelve of 14 dislocations occurred in cases with head-cup ratio < 0.7 (P = .013). Thirteen of 14 dislocations were in women (P = .005). ConclusionMaximizing the femoral head diameter per given cup size correlated with a decrease in dislocation rate in modern posterior approach THA. Furthermore, these results suggest that dual-mobility articulations should be reserved for high-risk patients or patients in whom a 40-millimeter femoral head is not possible. Level of EvidenceIV–consecutive case series; no control group.

EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks

The sharp increase in encrypted traffic brings a huge challenge to traditional traffic classification methods. Combining deep learning with time series analysis techniques is a recent trend in solving this problem. Most of these approaches only capture the temporal correlation within a flow. The accuracy and robustness are unsatisfactory, especially in an unstable network environment with high packet loss and reordering. How to learn a representation with a strong generalization ability for each encrypted traffic flow remains a key challenge. Our detailed analysis indicates that there is a graph with particular local structures corresponding to each type of encrypted traffic flow. Inspired by this observation, we propose a novel deep learning framework called EC-GCN to classify encrypted traffic flows based on multi-scale graph convolutional neural networks. We first provide a novel lightweight layer that only relies on the metadata and encodes each encrypted traffic flow into graph representations. So that our framework can be independent of different encryption protocols. Then we design a novel graph pooling and structure learning layer to dynamically extract the multi-graph representations and improve the capabilities to adapt to complex network environments. EC-GCN is an end-to-end classification model that learns representative spatial–temporal traffic features hidden in a traffic time series and then classifies them in a unified framework. Our comprehensive experiments on three real-world datasets indicate that EC-GCN can achieve up to 5%–20% accuracy improvement and outperforms state-of-the-art methods.

Off to a Good Start: Dynamic Contribution Patterns and Technical Success in an OSS Newcomer’s Early Career

Attracting and retaining newcomers are critical aspects for OSS projects, as such projects rely on newcomers’ sustainable contributions. Considerable effort has been made to help newcomers by identifying and overcoming the barriers during the onboarding process. However, most newcomers eventually fail and drop out of their projects even after successful onboarding. Meanwhile, it has been long known that individuals’ early career stages profoundly impact their long-term career success. However, newcomers’ early careers are less investigated in SE research. In this paper, we sought to develop an empirical understanding of the relationships between newcomers’ dynamic contribution patterns in their early careers and their technical success. To achieve this goal, we compiled a dataset of newcomers’ contribution data from 54 large OSS projects under three different ecosystems and analyzed it with time series analysis and other statistical analysis techniques. Our analyses yield rich findings. The correlations between several contribution patterns and technical success were identified. In general, being consistent and persistent in newcomers’ early careers is positively associated with their technical success. While these correlations generally hold in all three ecosystems, we observed some differences in detailed contribution patterns correlated with technical success across ecosystems. In addition, we performed a case study to investigate whether another type of contributions, i.e., documentation contribution, could potentially have positive correlations with newcomers’ technical success. We discussed the implications and summarized practical recommendations to OSS newcomers. The insights gained from this work demonstrated the necessity of extending the focus of research and practice to newcomers’ early careers and hence shed light on future research in this direction.

Measuring financial reporting quality of non-financial firms listed in Amman Stock Exchange

accuracy and transparency is vital for internal and external users, this study insures that financial reporting quality and financial information published for non-financial firms in ASE is not manubulated, accurate, combitable, timely published, adequate, and free of earning management, to be usefull for decision making for all users, especially for new investors. Methodology- The period 2010 – 2019 forms the data for the statistical analysis. In addition, and to measure the FRQ, the paper uses time series analysis techniques, including descriptive analysis, frequencies and multiple OLS regression were used. Using data from 910 firm-years observation of companies listed on Amman Stock Exchange for the period of 2010 to 2019. Findings- The analysis reveals that The probability for F-Statistics &lt; 0.05, this led to accept the statistical model. on the other hand, R square is 31.3 % which means that the explanatory power of modified Jones model (Dechow et al., 1995) reach 31.3% in explain the practices of earnings management in Jordanian companies. on the other hand, the VIF values are lower than 10 and tolerances higher than 10% which mean there is no multicollinearity problem in the model. as for the 1/A_(i,t-1) shows a significant and negative relationship with total accruals, were 〖∆(Sales-AR)〗_it/A_(i,t-1) and 〖PPE〗_it/A_(i,t-1) 1/A_(i,t-1) shows a significant and positive relationship with total accruals. Conclusion- the highest number of companies practicing earnings management in the year (2012 and 2014) was 60%, while the lowest number of companies that practice earnings management in the year (2015) and it was 32%, on the other hand, the highest number of companies do not practicing earnings management in the year (2015) was 68%, while the lowest number of companies that do not practice earnings management in the year (2012 and 2014) and it was 40%. Keywords: FRQ, modified jones model, Amman Stock Exchange, non-financial firms JEL Codes: G10, G12

Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review.

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.

Identifying Energy Inefficiencies Using Self-Organizing Maps: Case of A Highly Efficient Certified Office Building

Living and working in comfort while a building’s energy consumption is kept under control requires monitoring a system’s consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings.