Smoothed Time Series Research Articles

Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series

ABSTRACT Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

The link between the normalized difference vegetation index in major crops and meteorological factors

Normalized difference vegetation index (NDVI) is frequently used in monitoring of meteorological events. The goal of this study was to establish the relationship between the spatial NDVI with air temperature and precipitation amounts in major crops cultivated in the steppe zone of Ukraine, namely, winter wheat, winter rapeseed, grain corn, soybeans, and sunflower. The study included the croplands of eight regions: the Crimea, Kherson, Mykolaiv, Odesa, Zaporizhzhia, Dnipropetrovsk, Kirovohrad, and Kharkiv. The yield data were retrieved from the official bodies of the State Statistical Service of Ukraine. The meteorological data on monthly air temperature and rainfall were retrieved from regional hydrometeorological centers. The NDVI values were retrieved from the GIMMS Global Agricultural Monitoring System, which provides Terrain MODIS NDVI 8-Day smoothed time series with 250 m resolution. The study was performed for the 2021-2022 for winter rapeseed and sunflower; 2017 and 2020 for grain corn; 2022 for winter wheat; and 2017 for soybeans. We found almost no or weak correlation between monthly NDVI values of the studied crops and rainfall amounts. A stronger correlation was found for air temperature, with the greatest values of the correlation coefficient of 0.76, 0.72, –0.72 for sunflower, soybeans, and winter wheat, respectively. The linear regression models, developed to predict NDVI based on the air temperature for the mentioned crops, provided good prediction accuracy with the relative error within 10–20%. The best overall fit and accuracy was produced by the model of sunflower’s NDVI. Only winter crops were observed to have a negative correlation with air temperature, suggesting that the cultivated varieties of these crops are heat-intolerant.

Forecasting methods in Greek coastal shipping: The case of Southwest Crete

The aim of this paper is to exact the most effective model at capturing the seasonal and short-term components of passenger traffic in Southwest Crete coastal shipping. There has been no similar effort in the past. The passenger traffic forecast is crucial for the public and private sector, as it is necessary for decision making. In our analysis we considered the six largest ports of Southwest Crete. The seasonal repeated fluctuations and the quarterly observations made Winter’s triple exponential smoothing, time series decomposition, simple seasonal model, seasonal ARIMA model and Lis’ simplistic forecast suitable for our case. The results showed that in four of the six ports the Winters’ method is best adapted. The port of Gavdos adapts better to the decomposition method and the port of Sougia to Li’s method. No port led, through the seasonal ARIMA models or simple seasonal model, to better results. In most cases, traffic trend did not change over time, the seasonal component significantly affected the time series, and the time series smoothing was strong.

Pengendalian Persediaan Bahan Baku Petis Udang dengan Menggunakan Metode Economic Order Quantity (Eoq) di Ud. Agung Jaya

Raw material inventory is very important for companies. UD. Agung Jaya is a company that operates in the food sector, namely Petis Udang. Problems at UD. Agung Jaya has raw material stock problems, namely a shortage or excess of raw materials. The aim of this research is to help optimize UD costs of UD. Agung Jaya. This research uses raw material purchase data for shrimp heads, sugar, and flour which is processed by forecasting calculations for 12 periods or 1 year into the future using the single exponential smoothing (SES) time series forecasting method. The aim is to determine the need for raw materials so that there are no shortages or excesses of raw materials during the ongoing production process, and the efficiency of raw material needs in this research. The results of the research show that the raw material for shrimp heads has a mape error value of 24, sugar has a mape error value of 24, and flour has a mape error value of 26.1. Furthermore, the raw material decomposition method for shrimp heads has a mape error value of 15, sugar has a mape error value of 15, and flour has a mape error value of 16.42. The second result of forecasting shows that the decomposition method obtains a smaller mape value or the lowest error value. This research also uses the Economic Order Quantity (EOQ) method in the 2022-2023 period with raw material yields of 515.28 kg of shrimp heads, 488.54 kg of sugar and 188.09 kg of flour.

The Role of Smart City in Achieving Sustainable Development: Google Trends Analysis and Exponential Time Series Smoothing Models

The concept of sustainable smart urban development is an important element of the modern discourse on the future development of cities, regions, and the world as a whole, a key component of a strategy aimed at introducing innovations, improving the life quality of citizens, ensuring inclusiveness and sustainability of local development. It integrates the concept of Smart City as the application of advanced innovative technologies to solve urban problems and the concept of Sustainable Development as a balancing of socio-economic and environmental interests to achieve the Sustainable Development Goals. While discussing the problems and prospects introducing any social models or strategies, it is important to understand the level of public interest in this issue, the level of citizens’ awareness, the readiness to receive new information, and the readiness for change. The article is focused on the analysis of retrospective dynamics (from 01.01.2010 to 01.12.2023) and economic and mathematical modelling of forecast trends in changing public interest in the concepts of “Smart City” and “Sustainable Development” (identified using the Google Trends tool). The analysis was carried out for the world as a whole and for Ukraine specifically. The study identifies and describes the evolution of changes in public interest in these topics, identifies the time points of a sharp surge in interest, and conducts a comparative analysis in the content (trends for these two search queries) and spatial context (trends in Ukraine compared to global trends). The countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, and the countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, the TOP-10 are identified. The factors whose influence is relevant to the public interest dynamics are identified (consumer trends, media activity, dynamics of the introduction of relevant concepts, cultural, linguistic, and historical contexts, the general level of education, digital inclusion and consciousness of the population of different countries, etc.) Using the exponential smoothing method, models were built to predict the dynamics of time series – the number of queries from Ukrainian Internet users regarding the concepts of “Smart City” and “Sustainable Development”. Additive forecasting models, which differ in the trend component, are obtained.

HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022

Abstract. Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both “reprocessing” and “value-added data” with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or −0.17 to 0.78 or −0.06, respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series fluctuations. The time-series stability (TSS) index, which represents temporal stability, indicated that the area with smooth LAI time series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at https://doi.org/10.5281/zenodo.8296768 (Yan et al., 2023).

FLUCTUATIONS AND VOLATILITY OF WHITE EGGS CONSUMER PRICES IN THE MEXICO VALLEY

Objective: To develop a time series model and analyze the characteristic fluctuations of the average white eggs consumer prices in the Mexico Valley (AWECP), quantifying the seasonal and cyclical fluctuations of said prices. Design/methodology/approach: A Moving Average Time Series Smoothing method was used to reduce the variation of a data set, and separate cyclical, seasonal, and OLS variations to calculate the trend line. Results: The Seasonal Component (SC) indicates that in autumn-winter the AWECP is volatile above the annual average by 6.3 % while in spring-summer it declines by 9.13 % on average. The price volatility is largely explained by external factors and the biological process of poultry animals throughout one year. The cyclical component (CC) was 34 months and occurs irregularly. Limitations on study/implications: This research focused on finding the SC, T, and CC, but not their respective indices. To make short-term forecasts, it is necessary to calculate the indices and compare them to other time series analysis methodologies. Findings/conclusions: The analysis of the AWECP in the Mexico Valley indicates the presence of SC and an upward CC trend in the time series. The AWECP volatility has harmful effects on the basic basket of the middle and lower-class population in the Mexico Valley given that this food is the most complete, cheap, and accessible source of animal protein in the Mexican market.

Quantifying uncertainty: The benefits of removing snow cover from remote sensing time series on the extraction of climate-influenced grassland phenology on the Qinghai–Tibet Plateau

Vegetation phenology is a sensitive indicator of climate change. Remote sensing technology is often utilized to estimate vegetation phenology over expansive regions, but it contains uncertainties stemming from methods of data preprocessing and phenology extraction. Previous studies have primarily focused on individual causes of uncertainty, neglecting potential interactions between various causes and the consequent effects on analyzing vegetation's response to climate change. In this study, the uncertainty introduced by multiple time-series processing procedures in extracting the start of the growing season (SOS) of grassland on the Qinghai–Tibetan Plateau (QTP) was assessed using remote sensing data. This includes data preprocessing (time-series smoothing and snow cover removal) and phenology extraction. How these uncertainties might influence the analysis of SOS response to climate change was further quantified. Our findings demonstrated that the average uncertainty in grassland SOS extraction on the QTP for the period 2001–2015 reached 20.01 d, and snow-induced noise emerged as the principal factor affecting SOS estimates. Temporal trends of SOS revealed a regional average uncertainty of 1.06 d/y, both snow cover removal and the smoothing method were influential, each introducing uncertainty of 1.04 d/y and 1.05 d/y, respectively. After the removal of snow cover, uncertainties were reduced by 35 % for the SOS temporal trend. SOS response to climate change was extracted by various methods, leading to divergent and at times conflicting outcomes. The uncertainty for SOS sensitivity to temperature was averagely 10.50 d/°C on the QTP, while that for SOS sensitivity to precipitation was 1.75 d/mm. As the duration of snow cover lengthened, uncertainty increased in SOS response to temperature, while uncertainty increased in arid regions for SOS response to precipitation. The insights provided here contribute to enhancing phenology extraction accuracy and understanding the interactions between global climate change and vegetation phenology.

An integrated SWJ-LSTM-ETS modeling strategy for investigating upper morphology dynamics of a stochastic laboratory delta with environmental changes

Accurately modeling delta morphological evolution remains challenging because of the complexity of interacting processes (e.g., runoff, wave and tidal flow). This study develops an integrated framework combining a shallow water jet (SWJ) based model, long short-term memory (LSTM) neural networks, and exponential smoothing (ETS) time series models to predict the cross-sectional topographic dynamics of a stochastic experimental delta's upper plain. The integrated approach enhances explanatory power compared to individual models by assimilating mechanistic and empirical techniques. Hindcasting and forecasting under vegetation and intermittent discharge impacts yield insights aligning with validated findings on fluvio-morphological responses. Notably, new interactions are revealed like discharge inducing central deposition while vegetation drives bi-lateral erosion near the inlet. Farther downstream, discharge transitions result in alternating and pronounced patterns of erosion and deposition, with abrupt changes in discharge having a greater impact than gradual ones, particularly considering the mitigating effect of vegetation on these dynamics. This demonstrates an effective strategy widely applicable to integrating physics-based and data-driven models for analogous environmental systems involving both deterministic and random elements. The key contributions are developing a versatile integrated modeling methodology through synergizing physical simulators, statistical learners, and stochastic representations. This provides enhanced capabilities for elucidating complex delta dynamics.

An extended exponential SEMIFAR model with application in R

The article at hand provides a detailed description of the esemifar R-package, which is an extension of the already published smoots R-package, enabling the data-driven local-polynomial smoothing of trend-stationary time series with long memory. In this regard, a simple data-driven algorithm is proposed based on the well-known iterative plug-in algorithm for SEMIFAR (semiparametric fractional autoregressive) models. Two new functions for data-driven estimation of the trend and its derivatives in the presence of long memory are introduced. esemifar is applied to various environmental and financial time series with long memory, for example, mean monthly Northern Hemisphere temperature changes, daily observations of the air quality index of London (Britain), quarterly G7-GDP and daily trading volume of the S & P500. It is worth mentioning that this package can be applied to any suitable time series.

Examining the potential for early detection of spruce bark beetle attacks using multi-temporal Sentinel-2 and harvester data

Forests are invaluable terrestrial ecosystems with considerable economic, ecological, and environmental benefits. Bark beetles have been recognized as one of the major causes of forest disturbance, and climate change can exacerbate their impact, leading to more tree mortality. Early detection of bark beetle attacks is vital to reduce forest loss and devastating consequences. This study examines the potential for early detection of European spruce bark beetle (Ips typographus L.) attacks in southeastern Sweden using comprehensive harvester data and time series of Sentinel-2 images, 2015–2021. Specifically, it aims at 1) determining the most pronounced wavelength bands and vegetation indices (VIs) of Sentinel-2 for early detection, 2) determining the number of attacked trees in a Sentinel-2 pixel required to enable detection, 3) testing three change detection approaches, Detecting Breakpoints and Estimating Segments in Trend (DBEST), Mean-Level-Shift (MLS), and Cumulative Sum (CUSUM) to investigate the potential for early detection of bark beetle attacks. The greatest separation in reflectance between healthy and attacked pixels, from first swarming peak (May 2018) till harvesting (April 2019), was observed in the SWIR1 (0.018) and SWIR2 (0.011) bands followed by red-edge (0.008), red (0.007), NIR (0.005), and the green band (0.004). The blue band showed the least separation (0.003). All VIs showed a change in their base level after the swarming and this was more prominent for NDRS with an increase of 0.14, followed by NDWI (-0.13), CCI (-0.11) and NDVI (-0.09), all with decreasing values. The observed responses of VIs in relation to the number of attacked trees in a Sentinel-2 pixel increased gradually for pixels having one to ten infested trees, with the strongest response observed for pixels with 9 to 14 attacked trees. Pixels including more than 14 attacked trees did not show any further substantial change in VIs. DBEST, on average, indicated that the infestation impact on VIs is detectable one month after the swarming peak with a 15–31 days precision. MLS and CUSUM with up to two months' accuracies were ranked next. NDRS and CCI showed superior detection performance compared to NDVI and NDWI. The detection was based on smoothed time series of Sentinel-2 data to reduce the influence of noise and missing data and cannot be directly applied to a near real-time detection method.

Correlation of satellite-based LAI and actual crop yield

The main objective of this article was to investigate the correlation between actual crop yield and Sentinel-2 Leaf Area Index (LAI) for the further possibility of predict model creating. To do so, the following steps have been done. Step 1 – the dataset of actual crop yield was collected for 2364 fields in Ukraine represented with maize, soy, sunflower, winter wheat, winter rapeseed and winter barley. Step 2 – the dataset of Sentinel-2 LAI was collected for 2016-2018 period according to the actual crop yield available. Step 3 – LAI preprocessing (spatial averaging, temporal interpolation/extrapolation to fill the time series gaps, smoothing time series dynamics, temporal averaging). In order to accomplish the process of filling the gaps for the LAI time series, the regular time series dynamics of LAI with a 1-day interval were created using 4 methods: linear interpolation, spline interpolation, LOCF (Last Observation Carried Forward) and ARIMA (AutoRegressive Integrated Moving Average). The time series smoothing process have been accomplish using the local polynomial regression (LOESS) function with different degrees of smoothing. The LAI dynamics preprocessing step did not strongly affect the improvement of the correlation coefficients. Thus, the smoothing process for the time series LAI dynamics at the 0.1 degree of smoothing according to the LOCF and ARIMA gap-filling methods of improved correlation coefficients by 0.01 on average. Step 4 – actual yield values were related to preprocessed satellite-based LAI (correlation of actual yields and LAI). A strong relationship was not indicated (with averaged by vegetation periods correlation coefficient of 0.4 for maize, 0.52 – soy, 0.39 – sunflower, 0.86 – winter barley, 0.54 – winter rapeseed and 0.5 – winter wheat). Since the reliability of obtained correlation coefficients also depends on how many observed data points were in the sample, the hypothesis test of the "significance of the correlation coefficient" has been performed and shows the significance level of p < 0.05 for all crops except winter barley (there is insufficient evidence to conclude that high correlation coefficient of 0.86 for this crop is significant). The average correlation coefficient for all crops is about 0.5 (p < 0.05) which is considered low/moderate. Thus, an attempt to create a linear crop yield prediction model using only Leaf Area Index (LAI) derived from Sentinel-2 will not be effective (based on the cases considered).

The impact of the COVID-19 pandemic on new and old kinetic energy of industry from the perspective of high-frequency electricity consumption: Evidence from Henan Province

With the normalization of the epidemic situation, China’s economy often disturbed by the pandemic. Both the old and new growth drivers have been affected by the epidemic, and their development trend has dropped significantly. However, the new growth drivers have shown good capacity for industrial recovery, driving the rapid and steady growth of the economy. Therefore, an effective, intelligent, precise, and adaptable monitoring method to new and old industry kinetic energy should be taken, which not only improve the level of macroeconomic analysis, but also supply solid support for enterprises and governments to make reasonable decisions. This research uses weekly electricity consumption statistics to monitor the impact of the COVID-19 pandemic on new and old kinetic energy of industry in Henan Province in 2020. To make the statistics of different weeks comparable, this research uses exponential smoothing and Multiple-seasonal decomposition of time series by locally weighted scatterplot smoothing (MSTL) to correct and decompose the statistics. The new kinetic energy index, the old kinetic energy index and the industry kinetic energy index of major industries are calculated based on the corrected and decomposed statistics. The results show that the new kinetic energy has rapidly adjusted the industry development model based on its own industry characteristics to promote economic recovery.

Smoothness of time series: a new approach to estimation

The assessment of the risk of occurrence of extreme phenomena is inherently linked to the theory of extreme values. In the context of a time series, the analysis of its trajectory toward a greater or lesser smoothness, i.e. presenting a lesser or greater propensity for oscillations, respectively, constitutes another contribution in the assessment of the risk associated with extreme observations. For example, a financial market index with successive oscillations between high and low values shows investors a more unstable and uncertain behavior. In stationary time series, the upper tail smoothness coefficient is described by the tail dependence coefficient, a well-known concept first introduced by Sibuya. This work focuses on an inferential analysis of the upper tail smoothness coefficient, based on subsampling techniques for time series. In particular, we propose an estimator with reduced bias. We also analyze the estimation of confidence intervals through a block bootstrap methodology and a test procedure to prior detect the presence or absence of smoothness. An application to real data is also presented.

Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy

The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.

Smoothing method for unit quaternion time series in a classification problem: an application to motion data

Smoothing orientation data is a fundamental task in different fields of research. Different methods of smoothing time series in quaternion algebras have been described in the literature, but their application is still an open point. This paper develops a smoothing approach for smoothing quaternion time series to obtain good performance in classification problems. Starting from an existing method which involves an angular velocity transformation of unit quaternion time series, a new method which employ the logarithm function to transform the quaternion time series to a real three-dimensional time series is proposed. Empirical evidences achieved on real data set and artificially noisy data sets confirm the effectiveness of the proposed method compared with the classical approach based on angular velocity transformation. The R functions developed for this paper will be provided in a Github repository.

Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit

Oil palm plays a crucial role in agriculture and plantations in Indonesia as a commodity with high economic potential. Net Fresh Fruit Bunches (FFB) production is an essential desired outcome in an oil palm plantation. Net FFB is utilized as the primary raw material for the production of Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). The existing challenge is that companies seek to achieve precise quantities and timing for net FFB production in oil palm. One proactive measure to address this is by predicting the net FFB production. Therefore, the objective of this research is to forecast net FFB production by comparing triple exponential smoothing and fuzzy time series methods. Data processing results demonstrate that both forecasting methods yield excellent quality predictions for net FFB production. In the conducted testing, both methods achieved low forecast error values, with MAPE of 11.14670196% and 10.44596891% respectively. However, fuzzy time series exhibited a lower error value compared to the triple exponential smoothing method. Based on these findings, it can be concluded that fuzzy time series is the most reliable model for accurately predicting net FFB production. The advantage of fuzzy time series in forecasting net FFB production can provide significant benefits for companies in determining appropriate strategies for future planning.

Laser ablation mass spectrometry blast through detection in R.

Organisms that grow a hard carbonate shell or skeleton, such as foraminifera, corals or molluscs, incorporate trace elements into their shell during growth that reflect the environmental change and biological activity they experienced during life. These geochemical signals locked within the carbonate are archives used in proxy reconstructions to study past environments and climates, to decipher taxonomy of cryptic species and to resolve evolutionary responses to climatic changes. Here, we use laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) as a time-resolved acquisition to quantify the elemental composition of carbonate shells and skeletons. We present the LABLASTER (Laser Ablation BLASt Through Endpoint in R) package, which imports a single time-resolved LA-ICP-MS analysis, then detects when the laser has ablated through the carbonate as a function of change in signal over time and outputs key summary statistics. We provide two examples within the package: a fossil planktic foraminifer and a tropical coral skeleton. We present the first R package that automates the selection of desired data during data reduction workflows. This is achieved by automating the detection of when the laser has ablated through a sample using a smoothed time series, followed by removal of off-target data points. The functions are flexible and adjust dynamically to maximise the duration of the desired geochemical target signal, making this package applicable to a wide range of heterogenous bioarchives. Visualisation tools for manual validation are also included. LABLASTER increases transparency and repeatability by algorithmically identifying when the laser has either ablated fully through a sample or across a mineral boundary and is thus no longer documenting a geochemical signal associated with the desired sample. LABLASTER's focus on better data targeting means more accurate extraction of biological and geochemical signals.

PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN

One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.