Fitting Time Series Research Articles

Addressing multidimensional highly correlated data for forecasting in precision beekeeping

In recent years, there have been relevant advances in precision beekeeping. These advances are mainly focused on proposing sensor systems that collect crucial information for bee welfare, creating integrated architectures that allow beekeepers to monitor the current state of their hive through real-time data. However, there is a lack of predictive models that would allow beekeepers to anticipate specific events that endanger bee welfare and lead to a decline in productivity. Specifically, predictive approaches accounting for the high correlation among internal variables of beehives have not been developed to date. To address this research gap, multivariate predictive models, including auto-regressive state-space and time series models, have been implemented and applied to four different hives from the we4bee project. These models aim to predict the internal variables of beehives (four different temperatures, humidity, and weight) by utilizing the meteorological conditions to which the hives are exposed. A cross-validation adapted to time series data was employed for model generalization assessment. Prediction models based on vector time series exhibited superior performance in forecasting internal hive variables compared to multivariate auto-regressive state-space models. Overall, the approach based on the vector error correction model yielded the best balance between fit, prediction, and computational cost. The VEC-based approach produces predictions with maximum mean absolute errors of 177 (312)g in weight, 3.366 (3.802)% in humidity, and 1.122 (1.685)°C in temperature at 1 (3)-days ahead when dealing with beehives exhibiting a high degree of correlation in their internal variables. Moreover, the VEC-based approach requires less than a second to perform the time series fitting process, which makes it particularly interesting for application in big data environments. The integration of such models into a decision support system would meet the need of beekeepers to anticipate potential threats to the welfare of their bee colonies, streamlining their monitoring processes while eliminating the need for continuous inspections.

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

Remote sensing monitoring and potential distribution analysis of Spartina alterniflora in coastal zone of Guangxi.

In recent years, the continuous expansion of Spartina alterniflora (S. alterniflora) has caused serious damage to coastal wetland ecosystem. Mapping the coverage of S. alterniflora by remote sensing and analyzing its growth pattern pose great importance in controlling the expansion and maintaining the biodiversity of coastal wetlands in Guangxi. This study aimed to use harmonic regression to fit time series data of vegetation indices based on Landsat images, and the phenological features were extracted as the input of random forest model to distinguish S. alterniflora in coastal zone of Guangxi from 2009 to 2020. The influence of natural environmental factors on the distribution of S. alterniflora was evaluated by Maxent model, and the potential distribution was analyzed. The results showed that: (1) Based on the time series data of characteristic indices fitted by harmonic regression, the extraction of phenological features of S. alterniflora identification effect exhibited high accuracy (in the result of 2009, Overall Accuracy [OA] = 97.33%, Kappa = 0.95). (2) During 2009-2020, the S. alterniflora in coastal zone of Guangxi kept proliferating and expanding from east to west. The total area of S. alterniflora continued to increase while the growth rate showed a trend that increased first and then decreased. (3) The Maxent model shows good accuracy in simulating the habitat of S. alterniflora, with a potential distribution area of 14,303.39 hm2. The findings will be beneficial to the understanding of dynamic changes of S. alterniflora in coastal zone of Guangxi and provide a scientific reference for other coastal wetland studies on S. alterniflora expansion.

Toward Sustainable Fishing Practices in Indonesia: Defining a Catch Quota Allocation for Saddletail Snapper (Lutjanus Malabaricus) in the Arafura Sea

The Arafura Sea hosts the commercially significant fish species, Lutjanus malabaricus, commonly known as the saddletail snapper. We devised a quota allocation approach for sustainably capturing this species. This approach involved estimating the species’ life history parameters through calculations of the size at 50% maturity, as well as life history ratios (M/K and Lm/L∞) to figure out the Total Allowable Catch (TAC). We based our calculations on the Maximum Sustainable Yield (MSY) that came from the catch and landing data from Probolinggo Port. This helped us set a fishing quota that fits the situation in Indonesia. Our analysis revealed that the size at 50% maturity was 52 cm, slightly lower compared to the neighborhood waters. Using the Length Based-Spawning Potential Ratio (LB-SPR) model, we determined the stock’s SPR to be approximately 17%, which falls below the targeted healthy fish stock SPR reference of 40%. The estimated MSY stood at 2,307 tons, and the TAC calculation slightly exceeded the approach based on time series fitting. Given the notably low SPR estimate, we suggest a cautious approach, recommending an MSY of 2,307 tons per year for Probolinggo Port, emphasizing the importance of maintaining the fishing mortality rate within the sustainable threshold.

Radial velocity homogeneous analysis of M dwarfs observed with HARPS. I. Exoplanet detection and candidates

The census of planets around M dwarfs in the solar neighbourhood meets two challenges: detecting the best targets for the future characterisation of planets with ELTs, and studying the statistics of planet occurrence that are crucial to formation scenarios. The radial velocity (RV) method remains the most appropriate for such a census as it is sensitive to the widest ranges of masses and periods. HARPS, mounted on the 3.6 m telescope at La Silla Observatory (ESO, Chile), has been obtaining velocity measurements since 2003, and can therefore be used to analyse a very large and homogeneous dataset. We performed a homogeneous analysis of the RV time series of 200 M dwarfs observed with HARPS from 2003 to 2019 (gathering more than 15000 spectra), with the aim of understanding detectable signals such as stellar and planetary companions and activity signals. The RVs were computed with a template matching method before carrying out the time series analysis. First, we focused on the systematic analysis of the presence of a dominant long-term pattern in the RV time series (linear or quadratic trend and sine function). Then, we analysed higher-frequency perdiodic signals using periodograms of the residual time series and Keplerian function fitting. We found long-term variability in 57 RV time series (28.5<!PCT!>). This led to the revision of the parameters of the massive planet (GJ 9482 b), as well as the detection of four substellar and stellar companions (around GJ 3307, GJ 4001, GJ 4254, and GJ 9588), for which we characterised inclinations and masses by combining RV and astrometry. The periodic analysis allowed us to recover 97<!PCT!> of the planetary systems already published in this sample, but also to propose three new planetary candidates orbiting GJ 300 (7.3M$_ oplus $), GJ 654(5M$_ oplus $), and GJ 739 (39M$_ oplus $), which require additional measurements before they can be confirmed.

An Accident Prediction Model Based on ARIMA in Kuala Lumpur, Malaysia, Using Time Series of Actual Accidents and Related Data

Recently, there has been an emerging trend to analyse time series data and utilise sophisticated tools for optimally fitting time series models. To date, Malaysian industrial accident data is underutilised and lacks informative records. Thus, this paper aims to investigate the Malaysian accident database and further evaluate the optimal forecasting models in accident prediction. The model’s input was based on available data from the Department of Occupational Safety and Health, Malaysia (DOSH), from 2018 until 2021, with 80% of the dataset to train the models and the remaining 20% for validation. The negative binomial and Poisson distribution prediction showed a mean absolute percentage error (MAPE) of 33% and 51%, respectively. It indicated that the negative binomial performed better than the Poisson distribution in accident frequency prediction. The available time series accident data were gathered for four years, and stationarity was checked in R Studio software for the Augmented Dickey-Fuller test. The lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other error values were used to justify the best model, which was the ARIMA(2,0,2)(2,0,0)(12) model. The ARIMA models were considered after the data showed autocorrelation. The MAPE for both ARIMA in R and manual time series were 40% and 49%, respectively. Therefore, the accident prediction by using R Studio would outperform the manually negative binomial and Poisson distribution. Based on the findings, industrial safety practitioners should report accidents to DOSH truthfully in the era of digitalisation. It could enable future data-driven accident predictions to be carried out.

Veterans Crisis Line Contacts After the 988 Suicide and Crisis Lifeline Rollout

IntroductionThis study identifies changes in Veterans Crisis Line (VCL) contact volume following the 988 National Suicide Prevention Hotline rollout, and examines changes in contact volume for self-identified Veterans. MethodsVCL's Medora database was analyzed from July 2018-June 2023, fitting linear interrupted time series models to forecast trends after the July 2022 rollout of the 988 Suicide Prevention Hotline. Data analysis was performed from 2023-2024. ResultsAfter the 988 rollout, average monthly VCL contact volume increased by 5,388 contacts (8.2%). The number of contacts self-identifying as Veterans increased by 2,739 (6.2%), while the percentage of self-identifying Veteran contacts who could be linked to VHA records declined by 3.8%. ConclusionsThe 988 rollout was associated with increased VCL contact volume and broad changes in the profile of users. This underscores the importance of crisis services in adapting to dynamic user needs and highlights the potential of national suicide prevention initiatives to reach diverse populations.

Forecasting tourism demand with search engine data: A hybrid CNN-BiLSTM model based on Boruta feature selection

Using search engine data (SED) to forecast tourist flow is essential for management and security warnings at tourist attractions. Existing prediction models cannot effectively handle noise in the SED and external uncertainties. Thus, insufficient feature extraction may hinder the fitting of tourist flow time series. To improve the prediction accuracy, a forecasting method combining the Boruta algorithm (Boruta), bidirectional long short-term memory (BiLSTM), and a convolutional neural network (CNN) is proposed in this study. The model was tested using a monthly tourist flow dataset of three adjacent tourist attractions (Hongcun Village, Mount Huangshan, and Xidi Village) in Anhui Province, China, from January 2011 to March 2021. Forecasting for Hongcun Village shows that the proposed model performs best in three-step-ahead forecasting with a root mean square error (RMSE) of 2341.77 and a mean absolute percentage error (MAPE) of 2.85 %. A comparative analysis demonstrated that the hybrid CNN-BiLSTM model outperformed benchmark models. Meanwhile, Boruta reduced the RMSE and MAPE of the model by 59.93 % and 54.11 %, respectively, which were superior to those of the baseline methods. Furthermore, a robustness test was applied considering Mount Huangshan and Xidi Village as additional exercises for prediction. The robustness test confirmed that the proposed model has the proper generalization capability. Accordingly, the forecasting framework used in this study is robust and operational. This can facilitate a new approach for tourism demand forecasting with SED and assist operators in making managerial decisions.

Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold

Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user’s expertise, limits their applicability. Furthermore, the neglect of SAR’s potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples.

Modeling of Long‐Term Load Forecast in Jordan Based on Statistical Techniques

The paper proposes a mathematical model for long‐term load forecast (LTLF) based on parametric and time series statistical techniques. The flowchart of the proposed algorithm was also presented. The multiple linear regression (MLR) as well as the autoregressive integrated moving average (ARIMA) model with different model orders are employed in load forecasting. Historical data from 1990 to 2022 were utilized in the model implementation and validation. The input data imply gross domestic product (GDP), oil prices, population, and the energy from renewable energy projects as independent variables, and the annual peak load is the dependent variable. The results obtained by MLR show that population and GDP have a positive impact on electricity demand, whereas the oil price and the penetration of the renewable energy have a negative impact on electricity demand. In ARIMA, the load forecast is estimated based on error (residual) estimation that is determined based on the time lag operator and autoregressive and moving average coefficients. The ARIMA (p, d, and q) model with six different model orders is investigated. The Bayesian information criteria (BIC) and Akaike information criteria (AIC) indices are used as a measure in the selection of the appropriate model for prediction. The comparison between the six model’s scenarios shows that ARIMA (1, 1, 1) is the best model that fits the time series with minimum error and with the lowest BIC and AIC. The RMSE, MAPE, MSE, and MAE provided by ARIMA (1, 1, 1) are 3.57%, 2.55%, 0.13%, and 54.36, respectively, whereas the AIC and BIC are 386.8 and 394.0, respectively. A comparison between ARIMA and MLR shows that ARIMA is better than MLR in terms of time series fitting and error level. The LTLF forecast period covering the period 2024–2035 considers three scenarios of future development in the country: the normal (medium forecast), the optimistic (high forecast), and the pessimistic (low forecast). The forecast is made in the three directions (i.e., medium, high, and low) to avoid the ambiguity and uncertainty that may occur in the data input used in forecasting the electricity demand. A comparison between the MLR and ARIMA in the last year 2035 shows a small deviation between the two methods with the forecasted values in the range between 5480 MW and 5520 MW, respectively.

GARCH Models and Distributions Comparison for Nonlinear Time Series with Volatilities

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to examine the performance of different GARCH models in forecasting crude oil price volatilities using West Texas Intermediate (WTI) data. The models considered are the standard GARCH, Integrated GARCH (IGARCH), Exponential GARCH (EGARCH), and Golsten, Jagannathan, and Runkle GARCH (GJR-GARCH), each with normal distribution, Student’s t-distribution, and Generalized Error Distribution (GED). To evaluate the performance of each model, the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are used as the model selection criteria, along with forecast accuracy measures such as absolute error, root mean squared error (RMSE), and mean absolute error (MAE). Post-estimation tests, including the Autoregressive Conditional Heteroskedasticity Lagrange Multiplier (ARCH-LM) test and the Ljung-Box test, are conducted to ensure the adequacy of all models. The results reveal that all GARCH models are suitable for modeling the data, as indicated by statistically significant estimated parameters and satisfactory post-estimation outcomes. However, the EGARCH (1, 1) model, particularly with Student’s t-distribution, outperforms other models in both data fitting and accurate forecasting of nonlinear time series.

Power-Impact-Harmonic Characteristics Analysis and Modeling of Diversified Power Loads in Urban Power Grid

Different load models of the same load have different effects on power flow calculation, short circuit calculation, voltage stability, and frequency stability after power system accidents. In this paper, some power load components in urban power grids are modeled by analyzing and fitting the measured power curve of load components, and they are divided into the power model, impact model, and harmonic model according to their characteristics. The power model includes the power models of some household appliances, industrial motors, and arc furnaces. The impact model includes the time series fitting model and probability fitting model. According to the measured power curve of load components, the load model established in this paper can avoid some risks of power calculation caused by the absence of a detailed load model.

The Impact of the Deregulation of Covid-19 on the Pharmaceutical Industry

The current COVID-19 has lasted for a long time, and the Chinese government has relaxed the control of the pandemic after many considerations. Numerous researches about the COVID-19's effects on the economy and business are currently available. But few studies are on the pharmaceutical industry and the Deregulation. Therefore, this paper selects the Pharmaceutical and Biological Full Benefit as the research object. And then uses the ARIMA model to perform time series fitting and prediction of the closing price series in the past period. By comparing the predicted and real values, how the Deregulation affected the stock price of the biopharmaceutical sector could be determined. After modeling analysis, it is concluded that a positive effect has been produced by the Deregulation on the stock price of the pharmaceutical industry. Further study shows that there is a short-term lag in the impact, and the relative growth rate is not large. And the impact has been observed with certain short-term nature through the prediction results. This paper innovatively uses stock price fluctuations as an insertion point to explore the impact of the Deregulation on the pharmaceutical industry, which has profound practical guiding significance. For Chinese policy makers, it is suggested that the country should cooperate with the improvement and upgrading of the pharmaceutical industry. For investors, it is suggested that they should maintain a more stable and positive wait-and-see attitude towards the pharmaceutical industry.

Quantification of Vegetation Phenological Disturbance Characteristics in Open-Pit Coal Mines of Arid and Semi-Arid Regions Using Harmonized Landsat 8 and Sentinel-2

Open-pit mining activities inevitably affect the surrounding ecological environment. Therefore, it is crucial to clarify the disturbance characteristics of open-pit mining activities on the surrounding vegetation and scientifically implement ecological restoration projects. This study investigates the impact of open-pit coal mining in arid and semi-arid regions on surrounding vegetation from a vegetation phenology perspective. Initially, we construct a high-frequency time series of vegetation indices by Harmonized Landsat 8 and Sentinel-2 surface reflectance dataset (HLS). These time series are then fitted using the Double Logistic and Asymmetric Gaussian methods. Subsequently, we quantify three pivotal phenological phases: Start of Season (SOS), End of Season (EOS), and Length of Season (LOS) from the fitted time series. Finally, utilizing mine boundaries as spatial units, we create a buffer zone of 100 m increments to statistically analyze changes in phenological phases. The results reveal an exponential variation in vegetation phenological metrics with increasing distance from the mining areas of Heidaigou-Haerwusu (HDG-HEWS), Mengxiang (MX), and Xingda (XD) in northwest China. Then, we propose a method to identify the disturbance range. HDG-HEWS, MX, and XD mining areas exhibit disturbance ranges of 1485.39 m, 1571.47 m, and 671.92 m for SOS, and 816.72 m, 824.73 m, and 468.92 m for EOS, respectively. Mineral dust is one of the primary factors for the difference in the disturbance range. The HDG-HEWS mining area exhibits the most significant disruption to vegetation phenological metrics, resulting in a delay of 6.4 ± 3.4 days in SOS, an advancement of 4.3 ± 3.9 days in the EOS, and a shortening of 6.7 ± 3.5 days in the LOS. Furthermore, the overlapping disturbance zones of the two mining areas exacerbate the impact on phenological metrics, with disturbance intensities for SOS, EOS, and LOS being 1.38, 1.20, and 1.33 times those caused by a single mining area. These research results are expected to provide a reference for the formulation of dust suppression measures and ecological restoration plans for open-pit mining areas.

Fitting Time Series Models to Fisheries Data to Ascertain Age

The ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish ear bones, typically by marginal increment analysis. These patterns often take the shape of a sawtooth wave with an abrupt drop in accretion yearly to form an annual band and are typically validated qualitatively. Researchers have shown key interest in modeling marginal increments to verify the marks do, in fact, occur yearly. However, it has been challenging in finding the best model to predict this sawtooth wave pattern. We propose three new applications of time series models to validate the existence of the yearly sawtooth wave patterned data: autoregressive integrated moving average (ARIMA), unobserved component, and copula. These methods are expected to enable the identification of yearly patterns in accretion. ARIMA and unobserved components account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structures. The unobserved component model produced the best results (AIC: −123.7, MSE 0.00626), followed by the time series model (AIC: −117.292, MSE: 0.0081), and then the copula model (AIC: −96.62, Kendall’s tau: −0.5503). The unobserved component model performed best due to the completeness of the dataset. In conclusion, all three models are effective tools to validate yearly accretional patterns in fish ear bones despite their differences in constraints and assumptions.

DEVELOPMENT OF SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL WITH ERROR PROCESS

This research work investigated with error process. The auto covariance function maximum likelihood methods, iterative method and chi-squares test statistics is used to develop SARIMA model corrupted with error process that is used to estimate the true parameter of the SARIMA model. The forecast performance measurement and properties of error with different value are also investigated. Test of seasonal unit roots are also carried out in the work. The simulation, and real data of Zamfara state monthly Rain fall from 1998 to 2022 is used to validate the results with R - Statistical software version 4.1.1 and mini tab statistical software version 14. The result showed a significance p- value of 0.000, the proposed model provides a generalization and more flexible specification than the existing models of AR (1) error and ARMA (1,1) error in fitting time series processes in the presence of errors . Hence, the studies showed that, the finding is closely to the true parameter of the process and would be useful to researchers in the prediction and handling of natural calamities that disturb the otherwise stability of a system.

Quantifying Evidence for—and against—Granger Causality with Bayes Factors

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null – we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study.

Trends in Chinook salmon spawner abundance and total run size highlight linkages between life history, geography and decline

AbstractChinook salmon (Oncorhynchus tshawytscha, Salmonidae) are foundational to social‐ecological systems of the Northeast Pacific Rim and exhibit a rich diversity of life histories including in their adult migration timing, age at critical life‐history transitions and marine feeding distributions. In recent decades Chinook have experienced declines across much of their native range; however, changes in productivity and abundance have rarely been evaluated in relation to life‐history variation. To understand trends in Chinook salmon production, and how they are related to life history, we compiled time series data from the Fraser River to the Sacramento River on total run size (pre‐fishery abundance) and escapement (post‐fishery spawner abundance) and fit time series models to estimate trends across this bioregion. Our analysis revealed that most Chinook populations are declining, with negative trends in escapement (57 of 79) and total run (16 of 23) size. Trends were most acutely negative for interior spring Chinook in the Fraser, Columbia and Snake Rivers and most populations in California. Summer and fall Chinook had mixed trends, with several summer and fall upriver bright populations in the interior Columbia and Fraser exhibiting increases in abundance from the 1990s to 2019. Our research reveals widespread declines of this important species, but local complexity in trends that are mediated by population‐level life history, migratory behaviours and watershed‐scale restoration actions. Understanding linkages between life histories and resilience should inform rebuilding efforts for Chinook salmon and highlight the need to conserve intraspecific biodiversity.

Model identification via total Frobenius norm of multivariate spectra

Abstract We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density. We develop new asymptotic results for linear and quadratic functionals of the periodogram, and apply the integrated Frobenius norm to fit time series models and test whether model residuals are white noise. The case of structural time series models is addressed, wherein co-integration rank testing is formally developed. Both applications are studied through simulation studies and time series data. The numerical results show that the proposed estimator can fit moderate- to large-dimensional structural timeseries in real time.

Epidemiologic Assessment of Pediatric Inflammatory Bowel Disease Presentation in NYC During COVID-19.

Inflammatory bowel disease (IBD) pathogenesis is thought to be induced by a mix of genetic susceptibility, microbial populations, and immune triggers such as infections. Severe acute respiratory syndrome coronavirus 2 (SARS-nCoV2) may have increased capacity to generate autoimmune disease as evidenced by known spikes in diseases such as type 1 diabetes mellitus. Public health interventions like masking and closures additionally created remarkable drops in typical viral infections, with remarkable shifts in influenza-like illness reporting in 2020. This study aims to evaluate the impact of SARS-nCoV2 and associated interventions on pediatric IBD presentation in New York City using records of new diagnoses at a consortium of 4 institutions between 2016 and June 2022. We fit time series model (autoregressive integrated moving average model) to monthly and quarterly number of cases of each disease for January 2016-March 2020 and forecast the period between April 2020 and June 2022. We note no decrease in ulcerative colitis (UC) or Crohn disease (CD) in the aftermath of historic low levels of overall viral illness, and statistically significant increases in CD diagnoses and elevation in UC diagnoses creating a trend suggesting overall increase in IBD diagnoses exceeding the baseline rate of increase. These data suggest a possible linkage between SARS-nCoV2 infection rates and subsequent pediatric IBD presentation.