Search results

The purpose of this paper is to explore waiting times in improving access to psychological therapies (IAPT) services before and throughout the COVID-19 pandemic. The paper aims to help develop a better understanding of waiting times in IAPT so that interventions can be developed to address them.

IAPT national data reports was analysed to determine access and in-treatment waiting times before, during and after the COVID-19 pandemic. Time-series data was used to examine referral patterns, waiting list size and waiting times between the period of November 2018 and January 2022. The data covers all regions in England where an IAPT service has been commissioned.

There was a dramatic drop in referrals to IAPT services when lockdown started. Waiting list size for all IAPT services in the country reduced, as did incomplete and completed waits. The reduction in waiting times was short-lived, and longer waits are returning.

This paper aims to contribute to the literature on IAPT waiting times both in relation to, and outside of, COVID-19. It is hoped that the conclusions will generate discussion about addressing long waits to treatment for psychological therapy and encourage further research.

To the best of the authors’ knowledge, there is no published research examining the performance of IAPT waiting times to second appointment. The paper also contributes to an understanding of how IAPT waiting times are measured and explores challenges with the system itself. Finally, it offers an overview on the impact of the COVID-19 pandemic on waiting time performance nationally.

Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented.

In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified JAYA Algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output.

The performance analysis demonstrates that the deviation of FJA–DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error).

The proposed model can be used to forecast the open price details.

The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends

This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

This study aims to attempt to examine whether the increase in hotel room capacity in the USA had a significant impact on nationwide aggregated weekly revenue per available room (RevPAR) during the recession of 2007-2009 and forecast average RevPAR, Occupancy and Average Daily Rate (ADR) for 2013 and 2014.

Using Autoregressive Integrated Moving Average with Intervention analysis technique, this study examined the significance of the fluctuations in weekly RevPAR, room capacity and market demand through the recent recession and forecasted hotel performance for 2013 and 2014.

The results of time series analysis suggest that the fast growth of room capacity during the recession was one of the main causes of the decrease in RevPAR. The 9,878 more than expected increase in average weekly number of rooms probably caused at least $0.10 more than expected decrease in average weekly RevPAR. The findings of this study also suggest that the US lodging industry has been facing more severe oversupply since the recession and fully rebound of RevPAR cannot be expected in the very near future.

The findings of this study will help stakeholders make more informed decisions to cope with possible future economic downturns. By quantifying the capacity increase and forecasting future market demand, this study provides hotel investors with empirical evidence on the overdevelopment and insights into expected overall hotel performance in next two years. This study has also discussed the cyclical patterns of hotel development during the past two recessions.

By identifying overdevelopment as one of the main causes of RevPAR decrease during the recession, this study contributes to the literature by adding an alternative explanation of RevPAR fluctuations and deepens the understanding of the adverse effects overdevelopment has on the lodging industry. The findings of this study will help hotel investors develop more informed future expansion plans.

The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives.

Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index I_MVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the I_MVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error.

The construction of the index I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast I_MVM data, GM is the best performer among the 15 samples in the FY2018.

This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.

We analyze spatio-temporal data on U.S. unemployment rates. For this purpose, we present a family of models designed for the analysis and time-forward prediction of spatio-temporal econometric data. Our model is aimed at applications with spatially sparse but temporally rich data, i.e. for observations collected at few spatial regions, but at many regular time intervals. The family of models utilized does not make spatial stationarity assumptions and consists in a vector autoregressive (VAR) specification, where there are as many time series as spatial regions. A model building strategy is used that takes into account the spatial dependence structure of the data. Model building may be performed either by displaying sample partial correlation functions, or automatically with an information criterion. Monthly data on unemployment rates in the nine census divisions of the U.S. are analyzed. We show with a residual analysis that our autoregressive model captures the dependence structure of the data better than with univariate time series modeling.

This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time.

Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices.

The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth.

The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.

The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.

Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.

The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.

This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.

Single subject research has long been employed to evaluate intervention effectiveness with students with learning or behavioral disabilities. Typically, the results of single subject research are presented on graphic displays and analyzed by a method of visual inspection, in which analysts simultaneously consider such data elements as level change, slope change, and variability in baseline and treatment data. However, over the years several concerns regarding visual inspection have emerged, including relatively low inter-rater reliabilities. This chapter reviews the arguments in favor of visual inspection as an analytic tool, and also summarizes the arguments favoring statistical analysis of single case data. The use of randomization tests is recommended, and an example is provided of its use in research with students with learning and behavioral disorders.

The purpose of this research is to examine the way uncertainty plays a role in built land prices. This paper provides basic real option pricing models of land prices on the demand side in central Tokyo. The model in this research analyzes micro land prices covering individual lot data provided by the Land Price Index. Since land prices are determined by both macro economic environment and micro lot‐specific attributes, this paper utilizes both time‐series economic data and cross‐sectional lot‐specific data. The model incorporates both time‐series (macro) and cross‐sectional (micro) data including uncertainty terms. In addition to the total uncertainty in asset prices over years, this research also gives some ideas of cross‐sectional uncertainty in land price variations by utilizing cross‐sectional amenity variables. These cross‐sectional and time‐series variables including the two uncertainty variables are arithmetically combined and the OLS method is conducted. The data set consists of 4,368 land price data from 1985 through 2000. The results from the option‐based models favor the application of the real option theory in land prices. The total uncertainty with respect to built asset return has a substantial effect on increasing land prices, which implies that an increase in uncertainty leads to an increase in land prices.