Comparison Of Time Series Research Articles

Comparison of Fuzzy Time Series and ARIMA to Forecast Tourist Arrivals to Homestay in Pahang

Predictions of future events must be incorporated into the decision-making process. For tourism demand, forecasting is very important to help directors and investors to make decisions in operational, tactical, and strategic decisions. This study focuses on forecasting performance between Fuzzy Time Series and ARIMA to forecast the tourist arrivals in homestays in Pahang. The main objective of this study is to compare and identify the best method between Fuzzy Time Series and Autoregressive Integrated Moving Average (ARIMA) in forecasting the arrival of tourists based on the secondary data of tourist arrivals to homestay in Pahang from January 2015 to December 2018. ARIMA models are flexible and widely used in time-series analysis and Fuzzy Time Series which do not need large samples and long past time series. These two methods have been compared by using the mean square error (MSE) and mean absolute percentage error (MAPE) as the forecast measures of accuracy. The results show that Fuzzy Time Series outperforms the ARIMA. The lowest value of MSE and MAPE was obtained from using the Fuzzy Time Series method at values 2192305.89 and 11.92256, respectively.

Roles of advection and sediment resuspension-settling in the turbidity maximum zone of the Changjiang Estuary, China

Understanding mechanisms of suspended sediment transport in estuaries is critical for predicting estuarine geomorphic evolution, managing navigation and fisheries, and monitoring the health of estuarine ecosystems. Yet monitoring suspended sediment transport in estuaries is challenging due to the influences of both riverine and marine processes, and traditional in situ observations cannot directly differentiate contributions of multiple physical processes (e.g., advection and resuspension/settling). Here we investigated the effects of advection and sediment resuspension/settling on suspended sediment transport within the turbidity maximum zone at the South Passage of the Changjiang Estuary. The Changjiang catchment is notable for its long history of intensive human activities including dam construction, which has caused significant geomorphic changes along the river and its estuary. We conducted synchronized hydrodynamic measurements at three monitoring stations along the river channel and imported time series of current velocity and suspended sediment concentration (SSC) at these stations into a simplified, one-dimensional box model to separate in situ SSC into advection- and resuspension/settling-induced components. Our results show that high SSC consisted of a background SSC of 0.93 kg m−3 maintained by advection that contributes to 97.9% of the total average SSC, and a fluctuating SSC controlled by sediment resuspension and settling. The background SSC was higher than the fluctuating SSC at all times, indicating that sediment advection was stronger than local sediment resuspension in the river channel. We compared time series of SSC components, bed-level change and suspended sediment transport rates in the South Passage with previous observations in the nearby subaqueous delta, and found that the South Passage of the Changjiang Estuary experienced net accretion during summer because of the relatively high background SSC maintained by advection of fluvial sediment. In contrast, the subaqueous delta experienced erosion due to strong local sediment resuspension and weak sediment delivery by advection. Our results highlight the spatial variability in the magnitudes of sediment advection and resuspension/settling as key drivers of geomorphic changes of deltas and estuarine channels in response to sediment reduction caused by upstream dam construction.

Existing Causality Analysis Methods in Complex Brain-Physiological Dynamics: Comparative Time Series Study

Existing Causality Analysis Methods in Complex Brain-Physiological Dynamics: Comparative Time Series Study

Evidence That In-Service Professional Learning for Educational Leaders Matters

Educator leadership requires continuous development, including extended professional learning opportunities for principals in the field. This is also true for district leaders who likely have even fewer robust opportunities to grow professionally. We conducted a comparative interrupted time series of publicly available student achievement data from New Mexico to analyze the impact of a university-based organization that partners with district leaders to provide intensive professional learning opportunities for district leaders and school principals. We found positive impacts on student achievement in math and for English learners in English language arts. Implications for educator preparation and in-service programs are considered.

Combination of Using Pairwise Comparisons and Composite Reference Series: A New Approach in the Homogenization of Climatic Time Series with ACMANT

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that the differences between a candidate series and its neighbor series are usually analyzed instead of the candidate series directly, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e., composite reference series or pairwise comparisons. In composite reference series, the inhomogeneities of neighbor series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the Applied Caussinus-Mestre Algorithm for homogenizing Networks of climatic Time series (ACMANT) homogenization method, and tested in large, commonly available monthly temperature test datasets. Further favorable characteristics of ACMANT are also discussed.

Water Isotopic Signature of Surface Snow Metamorphism in Antarctica

AbstractWater isotope ratios of ice cores are a key source of information on past temperatures. Through fractionation within the hydrological cycle, temperature is imprinted in the water isotopic composition of snowfalls. However, this signal of climatic interest is modified after deposition when snow remains at the surface exposed to the atmosphere. Comparing time series of surface snow isotopic composition at Dome C with satellite observations of surface snow metamorphism, we found that long summer periods without precipitation favor surface snow metamorphism altering the surface snow isotopic composition. Using excess parameters (combining D,17O, and18O fractions) allow the identification of this alteration caused by sublimation and condensation of surface hoar. The combined measurement of all three isotopic compositions could help identifying ice core sections influenced by snow metamorphism in sites with very low snow accumulation.

National Early Warning Scores and COVID-19 deaths in care homes: an ecological time-series study

ObjectivesTo investigate whether National Early Warning Scores (NEWS/NEWS2) could contribute to COVID-19 surveillance in care homes.Setting460 care home units using the same software package to collect data on residents, from...

Comparison of Time Series of Integrated Aerosol Content in the Stratosphere and Total Ozone Content

Comparison of Time Series of Integrated Aerosol Content in the Stratosphere and Total Ozone Content

Let's do the time warp again: non‐linear time series matching as a tool for sequentially structured data in ecology

AbstractEcological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contain information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic time warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but also any sequentially structured data set, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small‐scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological data sets. In this study, we use an example data set of high‐resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith data sets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered “pseudo time series” data such as classification of species by shape.

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

Abstract. A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/, last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.

Network Inference of Transcriptional Regulation in Germinating Low Phytic Acid Soybean Seeds.

The low phytic acid (lpa) trait in soybeans can be conferred by loss-of-function mutations in genes encoding myo-inositol phosphate synthase and two epistatically interacting genes encoding multidrug-resistance protein ATP-binding cassette (ABC) transporters. However, perturbations in phytic acid biosynthesis are associated with poor seed vigor. Since the benefits of the lpa trait, in terms of end-use quality and sustainability, far outweigh the negatives associated with poor seed performance, a fuller understanding of the molecular basis behind the negatives will assist crop breeders and engineers in producing variates with lpa and better germination rate. The gene regulatory network (GRN) for developing low and normal phytic acid soybean seeds was previously constructed, with genes modulating a variety of processes pertinent to phytic acid metabolism and seed viability being identified. In this study, a comparative time series analysis of low and normal phytic acid soybeans was carried out to investigate the transcriptional regulatory elements governing the transitional dynamics from dry seed to germinated seed. GRNs were reverse engineered from time series transcriptomic data of three distinct genotypic subsets composed of lpa soybean lines and their normal phytic acid sibling lines. Using a robust unsupervised network inference scheme, putative regulatory interactions were inferred for each subset of genotypes. These interactions were further validated by published regulatory interactions found in Arabidopsis thaliana and motif sequence analysis. Results indicate that lpa seeds have increased sensitivity to stress, which could be due to changes in phytic acid levels, disrupted inositol phosphate signaling, disrupted phosphate ion (Pi) homeostasis, and altered myo-inositol metabolism. Putative regulatory interactions were identified for the latter two processes. Changes in abscisic acid (ABA) signaling candidate transcription factors (TFs) putatively regulating genes in this process were identified as well. Analysis of the GRNs reveal altered regulation in processes that may be affecting the germination of lpa soybean seeds. Therefore, this work contributes to the ongoing effort to elucidate molecular mechanisms underlying altered seed viability, germination and field emergence of lpa crops, understanding of which is necessary in order to mitigate these problems.

The Impact of Online Nutrition Education on WIC Client Retention and Redemption of the Cash Value Benefit of Fruit/Vegetables

Describe the effectiveness that online nutrition education has on WIC participants in maintaining program participation. All participants of the Indiana Special Supplemental Nutrition Program for Women Infant and Children (WIC) receive supplemental foods, nutrition education, and health care referrals. The Indiana WIC program established an online nutrition education program to help eliminate barriers to participants who are unable to be physically present for their second nutrition education appointments. The aim of this study was to compare the participation of WIC participants enrolled in standard in-person appointments with WIC participants enrolled in the Online Nutrition Education (ONE) pilot program by assessing the completion of the ONE lessons and the cash value benefit usage at 3 months after participants’ acceptance into the WIC program. A quasi-experiment was performed using a time series comparison of WIC participant attendance at their in-person nutrition education appointment to participants completion of an online nutrition education lesson. A second comparison of the two groups observed the participants redemption of their cash value benefit on fruits and vegetables (CVB). A total of 68,748 participants attended the traditional second nutrition education contact appointment during February 1 – September 7, 2019 and with the addition of the ONE pilot a total of 78,857 participants attended in 2020. The increase in participation from 2019 to 2020 was statistically significant (P=0.035). Analysis showed that there was a significant difference (P ≤ 0.001) between the 2019 and 2020 CVB mean redemption rates. This study provides evidence that WIC participants are more likely to maintain their participation when offered the addition of an online nutrition education appointment type.

The Australian ready-to-drink beverages tax missed its target age group

BackgroundDuring 2008 and 2009, the Australian Government increased the tax on ready-to-drink alcoholic beverages by 70% to discourage drinking among adolescents. MethodsTo evaluate the tax, we use the difference-in-difference and comparative interrupted time series estimators, where age is used to define the control and treatment groups. This methodology is applied to the Household Income and Labour Dynamics in Australia survey. ResultsWe show that the tax did not affect the alcohol consumption of those aged under 25 (the tax targeted age group) but substantially reduced drinking among those aged 25–69, reducing their average daily consumption of standard drinks by 8.9% from 2010 to 2018. ConclusionThe age group under 25 did not respond to the tax likely because of product substitution. Alcohol price policy may need to acknowledge complex substitute/complement relationships between beverages and consider a floor price on alcohol or a uniform volumetric tax per standard drink.

Targeting uncertainty in smart CPS by confidence-based logic

Since Smart Cyber–Physical Systems (sCPS) are complex and decentralized systems of dynamically cooperating components, architecture-based adaptation is of high importance in their design. In this context, a key challenge is that they typically operate in uncertain environments. Thus, an inherent requirement in sCPS design is the need to deal with the uncertainty of data coming from the environment. Existing approaches often rely on the fact that an adequate model of the environment and/or base probabilities or a prior distribution of data are available. In this paper, we present a specific logic (CB logic), which, based on statistical testing, allows specifying transition guards in architecture-based adaptation without requiring knowledge of the base probabilities or prior knowledge about the data distribution. Applicable in state machines’ transition guards in general, CB logic provides a number of operators over time series that simplify the filtering, resampling, and statistics-backed comparisons of time series, making the application of multiple statistical procedures easy for non-experts. The viability of our approach is illustrated on a running example and a case study demonstrating how CB logic simplifies adaptation triggers. Moreover, a library with a Java and C ++ implementation of CB logic’s key operators is available on GitHub.

Self-rated health trajectories: A dynamic time warp analysis

Self-rated health (SRH), individuals’ overall perception of their health, is a key predictor of health events. To target disease prevention efforts, it is important to understand how SRH develops over time. The goal of this short communication is to find prototypic SRH trajectories by applying dynamic time warping, a time series comparison technique initially developed for speech recognition. Revealing prototypic SRH trajectories can help direct disease prevention efforts towards trajectories that are more likely to result in adverse health events. Based on data from a Dutch representative sample of 2,154 individuals, our dynamic time warp analysis suggests that Dutch individuals do not typically show a steady growth or decline in SRH. Instead, we identified four relatively stable SRH trajectories that differed in average SRH. One of these trajectories is a path of consistent low SRH.

A comparative time series analysis of crude mortality rate in the BRICS countries

Proper research and analysis of mortality dynamics is essential to provide reliable economic information about any country. This paper deals with the historical comparative time series analysis of the mortality rate dynamics in the BRICS countries to determine their economic performances over the years. This article presents stochastic models based on autoregressive integrated moving average (ARIMA (p, d, q)) models of various orders with a view to identifying the optimal and comparative model for the crude death rate (CDR) in the BRICS countries. The ARIMA (p, d, q) models were formulated for the crude death rates in the BRICS countries and the overall annual crude death rate for the period 1960–2018. The optimal choice of ARIMA models of order p and q was selected for each of the series. The results indicate that the ARIMA (2, 2, 0) model was the optimal model for predicting mortality dynamics in the overall BRICS data. In addition, there was a significant decrease in trends (p-value < 2.22e-16) during the study period from 1960 to 2018. In addition, the crude death rate’s data for the BRICS countries proved to be mostly non-linear, non-seasonal and without structural breaks. Finally, the findings of this study were discussed and recognized as having relevant policy implications for forecasting, insurance planning, as well as for disaster or risk reduction in the context of unprecedented global happenings in the post-pandemic era.

Postural Analysis: Description of a Dedicated System

Background Dispite of postural analysis is a fundamental tool for evaluating the neuromusculoskeletal structure of a subject a unique formal analysis process has not yet been determined. The aim of this work is to propose a postural analysis protocol t promote discussion with other practitioners so as to make continuous improvements in the proposed protocol to provide a tool that can serve the needs of specialists even more efficiently. Materials and Methods The proposed system consists of four subsystems: medical history data, photographic references, overall observations, and tests. Results Even though the initial results are encouraging, additional use of the described analysis system is considered essential to improve the technique and the skills of the operator. In this light, specific requests and observations from other professionals are essential, and obtaining these requests and observations is one of the purposes of the present article, as initially declared. Conclusions The proposed postural analysis system represents a good balance of various, often opposing needs, being easily completed and reasonably fast and repeatable, making it possible to compare time series data.

Postural Analysis: Description of a Dedicated System

Background Dispite of postural analysis is a fundamental tool for evaluating the neuromusculoskeletal structure of a subject a unique formal analysis process has not yet been determined. The aim of this work is to propose a postural analysis protocol t promote discussion with other practitioners so as to make continuous improvements in the proposed protocol to provide a tool that can serve the needs of specialists even more efficiently. Materials and Methods The proposed system consists of four subsystems: medical history data, photographic references, overall observations, and tests. Results Even though the initial results are encouraging, additional use of the described analysis system is considered essential to improve the technique and the skills of the operator. In this light, specific requests and observations from other professionals are essential, and obtaining these requests and observations is one of the purposes of the present article, as initially declared. Conclusions The proposed postural analysis system represents a good balance of various, often opposing needs, being easily completed and reasonably fast and repeatable, making it possible to compare time series data.

Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Challenges with Evaluating Education Policy Using Panel Data during and after the COVID-19 Pandemic

Panel data methods, which include difference-in-differences and comparative interrupted time series, have become increasingly common in education policy research. The key idea is to use variation across time and space (e.g., school districts) to estimate the effects of policy or programmatic changes that happen in some localities but not others. In this commentary we highlight some specific challenges for panel or longitudinal studies of K-12 education interventions during and following the COVID-19 pandemic. Our goal is to help researchers think through the underlying issues and assumptions, and to help consumers of those studies assess their validity.