Chapter 21 - Statistical forecasting—regression and time series analysis

Introduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.

Cognitive Correlates of Impaired Testamentary Capacity in Alzheimer's Dementia.

The Indian commodity market is characterized by high volatility. When considering the agro-based commodity market, the prices may sometimes vary on a daily basis and regional basis. For the purpose of our research, we have restricted our region of study to the Indian national capital New Delhi. This paper aims to find out whether commodity markets follow a pattern with respect to prices, and if they do, then whether this could be determined by using basic fractal theory and determination of Hurst exponent. We have followed a suitable algorithm to find the Hurst exponent using statistical methods, specifically linear regression and time series analysis, wherein time is the independent variable and price of the commodity considered is dependent. The reason why time series analysis is chosen is because of the tendency of a time series to regress strongly to its mean. A statistical measure chosen to classify time series is the Hurst exponent. Initially, we have focused on onion prices for the years 2013 to 2017. The data set has been derived from the official website of the Consumer Affairs Department of the Government of India. The daily retail prices for Delhi for the month of June were observed and analyzed. We eventually aim to investigate if the market for onions has a long-term memory and will it be suitable to extend this conclusion to all other agro-based commodities. Our study has been motivated by the Fractal Market Hypothesis (FMH) that analyses the daily randomness of the market. We seek to find out whether the commodity market follows such a pattern provided that external factors remain constant. By external factors, we mean the variations that occur in the market with time, which include the demand, inflation, global price change, changes in the economy, etc. Keeping this in mind, we have attempted a time series analysis, using the monofractal analysis, at the end of which we would be estimating the Hurst exponent. The determination of Hurst exponent will help us to classify the time series as persistent or anti-persistent, i.e., how strong is the tendency of the time series to revert to its long-term mean value. Further, the multifractal analysis has been used to detect small as well as large fluctuations within the time series taken into consideration. This result would thus lead us to understand if prices in the commodity market could be remotely predicted, and what is the strength of the time series to return to its long-term mean value. Hence, this fractal analysis can be used to determine the characteristics of the prices in an agro-based economy.

Objective: The aim of this study was to investigate the effect of synthetic cannabinoids (SC) on P-wave dispersion (PD) in patients who consume SC. Materials and Methods: The study population included 72 patients who consumed SC and 36 age- and sex-matched healthy controls. The severity of addiction was detected using the addiction profile index (BAPI). The PD was measured by 12-lead ECG obtained upon admission to hospital. Statistical analyses were performed using the SPSS v20.0 statistical software package. Results: The mean age of the patients and controls was 26.9 ± 7.0 and 26.3 ± 6.5 years, respectively. Mean duration of SC consumption was 1.7 ± 0.7 years. Mean BAPI score of patients who consumed SC was 12.8 ± 3.4. Patients who consumed SC had a significantly higher PD value than controls (37.7 ± 11.5 vs. 30.6 ± 6.4 ms, p < 0.001). The BAPI score was significantly correlated with PD value (r = 0.675, p < 0.001). In the linear regression model that included PD value, age and heart rate, PD value was significantly and independently correlated with BAPI score (r² of the model = 0.339; p < 0.001). Conclusions: In this study, patients who consumed SC had significantly higher PD values than controls, and the BAPI score correlated with the PD value. Hence SC consumption could lead to an increased risk of cardiovascular disease through prolonged PD. We recommend the use of the simple and inexpensive ECG to assess cardiovascular risk in patients who consume SC.

Time Series Forecasting Using Wavelet Denoising an Application to Saudi Stock Index

BackgroundThe burden of diabetes-related deaths reached two million in 2019 globally. Accessibility to health care services and adherence to follow-up and therapy are key to improving outcomes for diabetic patients. We aimed to assess outpatient department (OPD) service utilization and diabetes-related hospitalizations over a period of 44 months.MethodsA retrospective cohort study was conducted on OPD visits and hospitalizations recorded between January 1, 2018, and August 31, 2021, at the St Luke Catholic Hospital (Ethiopia). All diabetic patients were included in the analysis. A linear regression model was used for univariate analysis of OPD visits and hospitalizations and their association with potential predictors. The autoregressive integrated moving average (ARIMA) method was applied to both the time series of OPD visits and hospitalizations. Potential predictors were sociodemographic factors, COVID-19 cases, mean monthly temperature and precipitations.ResultsIn the time series analysis, OPD visits increased over time (P < 0.01) while hospitalizations were stable. The time series model was ARIMA (0,1,1) for OPD visits and ARIMA (0,0,0) for hospitalizations. There were 1685 diabetes OPD patients (F = 732, 43%). Females had an average of 16% fewer OPD accesses per month (P < 0.01) and a lower number of hospitalizations per month (P = 0.03). There were 801 patients missing follow-up (48%). The time between follow-up increased with age (P < 0.01). OPD visits decreased differently by geographic area as COVID-19 cases increased (P < 0.01). There were 57 fewer forecast OPD visits per month on average using COVID-19 cases as ARIMA regressor. The odds ratio (OR) of new diagnosis at hospitalization was lower in patients with type 2 diabetes (OR = 0.26, 95% CI = 0.14-0.49, P = 0.02).ConclusionsDespite an increase in OPD visits for diabetic patients over the study period, the number of losses at follow-up and diagnoses at hospitalization remains high. Female sex, older age, and COVID-19 were associated with impaired OPD service accessibility. Primary health care should be implemented to achieve better health coverage and improve diabetes management.

This study aimed at determining the applicability planned-behaviour theory to predict the consumption of pearl vitamin D3 (1000 IU) among pregnant women in Iran in 2020. The participants of the present study were 200 pregnant women who were selected through random cluster sampling. In addition, data collection was done through the use of questionnaire and the proceeding analyses including correlation and linear regression were conducted using SPSS (version 20) (SPSS Inc., Chicago, IL) statistical software package. The findings showed that all three structure attitudes (r = 0.69, p<.001), subjective norms (r = 0.58, p<.001) and perceived behavioural control (PBC) (r = 0.52, p<.001) had significant correlations with consumption. Among the variables entered into the regression model, attitude, subjective norms and PBC were able to predict a total of 65% of pearl vitamin D3 (1000 IU) consumption variance among participants (F = 60.75, R = 0.66, R 2=0.65). According to the findings, TPB and its constructs were used to predict pearl vitamin D3 consumption in pregnant women in Iran. This outcome indicates that efforts to promote behavioural intentions through targeting subjective norms, attitude and PBC may promote pearl vitamin D3 consumption. Impact Statement What is already known on this subject? The results of this study showed predictors of pearl vitamin D3 consumption in pregnant women based on the theory of planned behaviour (TPB). Women receive adequate and proper knowledge, along with a positive attitude towards taking pearl vitamin D3, and feel that taking pearl vitamin D3 is at their discretion in terms of environmental factors (facilities and barriers), increases intend to use pearl vitamin D3. What do the results of this study add? The results of this study also showed that the amount of pearl vitamin D3 intake during pregnancy increased by pregnant women and decreased risk of foetal growth retardation, low birth weight, decreased risk of respiratory infections in infants, decreased asthma and eczema in infants. What are the implications of these findings for clinical practice and/or further research? The study showed the importance of the role of education based on TPB in consumption promoting pearl vitamin D3.

Time series shape association measures and local trend association patterns

Review for "Linear budget management in internet of things and &lt;scp&gt;6G&lt;/scp&gt; network environments: Linear regression and time series analysis"

Author response for "Linear budget management in internet of things and &lt;scp&gt;6G&lt;/scp&gt; network environments: Linear regression and time series analysis"

This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.

To design a statistical software package to provide automated calculations of normal and weighted and 3 indices.Prompted by the lack of commonly available software to compute weighted kappa and the nonproportionate workload needed to calculate our 3 variability indices manually, the new statistical software package was designed. To demonstrate the performance of the new CONQUISTADOR software, a simulation study (both intralaboratory and interlaboratory) was designed using 5,000 clinical samples randomly selected from a data file of > or = 200,000 conventional Pap smears and programmed to become "analyzed" by 12 cytologists in 5 imaginary laboratories.A representative set of both complete and partial outputs provided by the software, in Excel format (Microsoft, Redmond, Washington, U.S.A.) are shown to illustrate the different functions of the program. In the interlaboratory mode, the software calculates accuracy indicators (sensitivity, specificity, positive and negative predictive value, and their 95% CI), which are not common features of regular statistical packages; kappa and weighted kappa; and their 95% CI (comparison of single laboratories to all laboratories and pairwise comparisons between single laboratories). The 3 diagnostic variability indices can be computed separately for all samples or for only the positive samples. In the intralaboratory mode, the software calculates the same indices for individual cytologists.The CONQUISTADOR statistical package has properties that are useful in monitoring cytologic laboratory quality in both intralaboratory and interlaboratory settings. The software will be distributed by the National Institute of Health, Rome, for the delivery costs only.

Datasets are often derived by manipulating raw data with statistical software packages. The derivation of a dataset must be recorded in terms of both the raw input and the manipulations applied to it. Statistics packages typically provide limited help in documenting provenance for the resulting derived data. At best, the operations performed by the statistical package are described in a script. Disparate representations make these scripts hard to understand for users. To address these challenges, we created Continuous Capture of Metadata (C2Metadata), a system to capture data transformations in scripts for statistical packages and represent it as metadata in a standard format that is easy to understand. We do so by devising a Structured Data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a large fraction of data manipulation performed in practce. We then implement SDTA, inspired by relational algebra, in a data transformation specification language we call SDTL. In this demonstration, we showcase C2metadata's capture of data transformations from a pool of sample transformation scripts in at least two languages: SPSS and Stata (SAS and R are under development), for social science data in a large academic repository. We will allow the audience to explore C2Metadata using a web-based interface, visualize the intermediate steps and trace the provenance and changes of data at different levels for better understanding of the process.

The aim of this research is time series modeling of road traffic accidents in the west Arsi zone, Ethiopia, it focused on monthly traffic accidents from January 2016 to December 2020. The goal of this study was to explore the number of traffic accidents to fit a time series model for the monthly number of road traffic accidents and to forecast a tow year ahead of the number of road traffic accidents. The analysis was done by using statistical software packages using this software and knowledge of time series analysis, trend, ACF, PACF, and Box-Jenkins analysis were computed. From the trend plot, the road traffic accidents was fluctuate from month to month as well as from year to year. There was total accident fluctuation from month to month (not stationary) a total of 1010 RTA were observed. The mean of 16.83 and standard deviation of 5.764 for the total accident was served the minimum and maximum record of road traffic accidents is 4 and 33 respectively. By differencing data one time, (2, 1, 3) model was fitted for making a two-year ahead forecast. Proper model adequacy checking was done. Two-year ahead forecasts showed that October, January, and April 2021 are the months with the most prominent values. Even if the trend in total accidents was decreasing there is still a need to pay more attention in order to prevent the occurrence of accidents related to road traffic accidents.

Background: The coronavirus has killed over 80 million individuals globally. Thus, the linear regression and autoregressive integrated moving average (ARIMA) model analyze the pattern of COVID-19 and identify the future confirmed cases. Methods: In this study, the dataset was used from the Johns Hopkins University (JHU CSSE) data repository in COVID-19 analytics package and prophet library. The time series analysis creates a simulating linear regression and ARIMA model for COVID-19 confirmed cases. The best fit model is select by Akaike information criteria (AIC) and predicts short-term issues validated by Ljung-Box Q test using RStudio Cloud. Results: The linear regression and ARIMA model identifies a best-fit model for time series data. From this model, forecast of more than 300,000 to 1,500,000 from 2020 to 2022. In addition, it depicts a significant increasing trend in the future predictions of confirmed cases. Conclusion: This forecast can help estimate the number of cases that information can provide control measures for an epidemic outbreak. It can suggest the government plan the policies regarding the control of the spread of the virus.KeywordsTime seriesConfirmed casesLinear regressionARIMACOVID-19Predication