Comparison of Weighted Lag Adaptive LASSO with Autometrics for Covariate Selection and Forecasting Using Time-Series Data

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO (WLAdaLASSO) with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed with varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast.

Cohort's Analysis in eCommerce

Cohort analysis treats an outcome variable as a function of cohort membership, age, and period. The linear dependency of the three temporal dimensions always creates an identification problem. Resolution of this problem requires external knowledge that is often difficult to acquire. Most satisfactory is the introduction of variables held to measure the dimensions that underlie at least one of age, period and cohort. Such measured, substantive variables can provide direct tests of cohort-based explanations. A Promising path for future technical development is a hierarchical Bayes approach, which treats appropriately defined cohort, age, and period contrasts as randomly distributed and allows for their dependence on substantive, measured variables. Models that include age, period, and cohort can also include interactions between these dimensions, but not all such interactions are identified. This extends the realism of cohort models, since many phenomena seem to require specifications that allow for interactions between two or more of age, period, and cohort. Panel studies and cross-sectional studies with retrospective information not only support cohort analyses, they engender them. These longitudinal data structures do not, however, provide the basis for a solution to the identification problem.[5]

Analysis on Thermal Runaway Behavior of Prismatic Lithium-Ion Batteries with Autoclave Calorimetry

A total number of 25 different types of prismatic lithium-ion cells with a capacity between 8 and 145 Ah are examined in an autoclave calorimetry experiment in order to analyze their behavior during thermal runaway (TR). The safety relevant parameters such as mass loss, venting gas production and heat generation during TR are determined in two experiments per cell type and the results are compared to literature. An approximately linear dependency of the three parameters on the cell capacity is observed and hence correlations are derived. Due to the wide range in cell properties the correlations can be used as input for simulations as well as to predict the behavior of future battery cells within the property range of those tested and therefore contribute to the design of a safer battery pack.

Strength of Superelastic NiTi Velcro-Like Fasteners

Velcro hook-and-loop fasteners invented more than 70 years ago are examples of the mechanism inspired by the tiny hooks found on the surface of burs of a plant commonly known as burdock. Several years ago, a novel Velcro-like fastener made of two arrays of hook-shaped thin NiTi wires was developed. Unique features of such fasteners, such as high thermally-tunable strength, fair force–stroke reproducibility, forceless contact or silent release, all derive from the superelasticity of the NiTi micro-wires. Recently, it was noticed that the presented fastener design allowed for a prediction of the number of active hooks. In this continuing study, the tension strength of the fastener was simulated as a function of hook density. Based on statistics, the model showed non-linear dependency of the number of interlocked hooks, N, on the hook density, m (N = round (0.21 m + 0.0035 m2 − 6.6)), for the simple hook pairs and the given hook geometry. The dependence of detachment force on stroke was simulated based on the Gaussian distribution of unhooking of individual hook connections along the stroke. The strength of the studied NiTi hook fasteners depended on hook density approximately linearly. The highest strength per cm2 reached at room temperature was 10.5 Ncm−2 for a density of m = 240 hooks/cm2.

A Deep Vector Quantization Clustering Method for Polarimetric SAR Images

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well.

FACTORS AFFECTING CREDIT SPREAD IN INDIAN DEBT MARKET

This paper shows that how Macro risk factors affect the credit spread in the Indian debt market. Credit spread is the difference between government bonds and corporate bonds of the same maturity. Various factors impact the spread directly and indirectly. The main focus of this paper to determine the relationship between these factors and find out which factors are explaining credit spread. This paper determines the significance linear dependency of credit spread on various factors through regression analysis. These factors are the market risk factors such as Inflation, GDP growth, and liquidity factors like the Repo rate. This paper will show that whether we are accepting the null hypothesis which states that these factors affect the credit spread or reject the hypothesis of no impact of variables on credit spread

Growth rate and configurational entropy in Tsallis holographic dark energy

In this work, we analyzed the effect of different prescriptions of the IR cutoffs, namely the Hubble horizon cutoff, particle horizon cutoff, Granda and Oliveros horizon cut off, and the Ricci horizon cutoff on the growth rate of clustering for the Tsallis holographic dark energy (THDE) model in an FRW universe devoid of any interactions between the dark Universe. Furthermore, we used the concept of configurational entropy to derive constraints (qualitatively) on the model parameters for the THDE model in each IR cutoff prescription from the fact that the rate of change of configurational entropy hits a minimum at a particular scale factor $$a_{DE}$$ a DE which indicate precisely the epoch of dark energy domination predicted by the relevant cosmological model as a function of the model parameter(s). By using the current observational constraints on the redshift of transition from a decelerated to an accelerated Universe, we derived constraints on the model parameters appearing in each IR cutoff definition and on the non-additivity parameter $$\delta $$ δ characterizing the THDE model and report the existence of simple linear dependency between $$\delta $$ δ and $$a_{DE}$$ a DE in each IR cutoff setup.