Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of singular spectrum analysis (SSA) methods, namely recurrent SSA (RSSA) and vector SSA (VSSA), in univariate (UV) and multivariate (MV) frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the performance of the SSA-based models with classical and Bayesian variants of the autoregressive (AR) and vector AR models. Using an out-of-sample period of 1979:8–2014:6, given an in-sample period of 1973:1–1979:7, we find that the UVVSSA is the best performing model for the aggregate US home sales, while the MV versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results highlight the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.     

Incremental labeling in closed-2PM model

We consider an incremental optimal label placement in a closed-2PM map containing points each attached with a label. Labels are assumed to be axis-parallel square-shaped and have to be pairwise disjoint with maximum possible length each attached to its corresponding point on one of its horizontal edges. Such a labeling is denoted as optimal labeling. Our goal is to efficiently generate a new optimal labeling for all points after each new point being inserted in the map. Inserting each point may require several labels to flip or all labels to shrink. We present an algorithm that generates each new optimal labeling in O(lgn+k) time where k is the number of required label flips, if there is no need to shrink the label lengths, or in O(n) time when we have to shrink the labels and flip some of them. The algorithm uses O(n) space in both cases. This is a new result on this problem.     

Discriminative functional analysis of human movements

This paper investigates the use of statistical dimensionality reduction (DR) techniques for discriminative low dimensional embedding to enable affective movement recognition. Human movements are defined by a collection of sequential observations (time-series features) representing body joint angle or joint Cartesian trajectories. In this work, these sequential observations are modelled as temporal functions using B-spline basis function expansion, and dimensionality reduction techniques are adapted to enable application to the functional observations. The DR techniques adapted here are: Fischer discriminant analysis (FDA), supervised principal component analysis (PCA), and Isomap. These functional DR techniques along with functional PCA are applied on affective human movement datasets and their performance is evaluated using leave-one-out cross validation with a one-nearest neighbour classifier in the corresponding low-dimensional subspaces. The results show that functional supervised PCA outperforms the other DR techniques examined in terms of classification accuracy and time resource requirements.     

Weighted two-valued digit-set encodings: unifying efficient hardware representation schemes for redundant number systems

We introduce the notion of two-valued digit (twit) as a binary variable that can assume one of two different integer values. Posibits, or simply bits, in {0,1} and negabits in {-1,0}, commonly used in two's-complement representations and (n,p) encoding of binary signed digits, are special cases of twits. A weighted bit-set (WBS) encoding, which generalizes the two's-complement encoding by allowing one or more posibits and/or negabits in each radix-2 position, has been shown to unify many efficient implementations of redundant number systems. A collection of equally weighted twits, including ones with noncontiguous values (e.g., {-1,1} or {0,2}), can lead to wider representation range without the added storage and interconnection costs associated with multivalued digit sets. We present weighted twit-set (WTS) encodings as a generalization of WBS encodings, examine key properties of this new class of encodings, and show that any redundant number system (e.g., generalized signed-digit and hybrid-redundant systems), including those that are based on noncontiguous and/or zero-excluded digit sets, is faithfully representable by WTS encoding. We highlight this broad coverage by a tree chart having WTS representations at its root and various useful redundant representations at its many internal nodes and leaves. We further examine how highly optimized conventional components such as standard full/half-adders and compressors may be used for arithmetic on WTS-encoded operands, thus allowing highly efficient and VLSI-friendly circuit implementations. For example, focusing on the WBS-like subclass of WTS encodings, we describe a twit-based implementation of a particular stored-transfer representation which offers area and speed advantages over other similar designs based on WBS and hybrid-redundant representations.     

Influence of roasting temperature on reactions between ferruginous blende and copper(II) oxide

Thermal treatment in an oxidizing atmosphere of a mixture of raw blende (ZnS) and copper(II) oxide enhanced the formation of a mixed ferrite phase at temperature about 600°C. The composition established at 900°C was approximately (0.8 ZnO · 0.2 CuO)Fe₂O₃. For the mixture ZnS–CuO, with 5 to 15% CuO by weight, less than 15% copper was involved in the formation of the spinel phase. The increasing copper content in the mixture, roasted above 1000°C, favoured solubilization of iron, even in a weakly acid medium.     

A novel two-stage genetic algorithm for a mixed-model U-line balancing problem with duplicated tasks

A widespread supposition on mixed-model assembly line-balancing problems assigns a task, which is shared between two or more models to a single station. Bukchin and Rabinowitch (European Journal of Operational Research, 174:492–508, 2006) relaxed the restriction for mixed-model straight-line assembly line problems and allowed tasks common to multiple models to be assigned to different stations, called task duplication. In this paper, considering the same relaxation but for mixed-model U-shaped assembly lines, a novel genetic algorithm (GA) approach for solving large-scale problems is developed. Although superiorities of U-shaped assembly lines over straight lines have been discussed in several articles, this paper makes the advantage more tangible by providing a quantitative example. This paper also presents a novel two-stage genetic algorithm which is fittingly devised for solving the new proposed model. In order to evaluate the effectiveness of the GA, one small-scale and one medium-scale problem are solved using both the proposed GA and Lingo 8.0 software, and the obtained outcomes are compared. The computational results indicate that the GA is capable of providing high-quality solutions for small- and medium-scale problems in negligible central processing unit (CPU) times. It is worth mentioning that, for large-scale problems, such as Kim and Arcus test problems, no analogous results for those obtained by our proposed GA exist. To conclude, it can be said that the proposed GA performs well and is able to solve large-scale problems within acceptable CPU times.     

Optical properties of sol–gel dip-coated Ta2O5 films for electrochromic applications

Amorphous Ta₂O₅ films were prepared by sol–gel dip process on different substrates. The dip-coating technique was used to prepare amorphous Ta₂O₅ films by hydrolysis and condensation of tantalum ethoxide, Ta(OC₂H₅)₅, precursor. Stable coating solutions were prepared using acetic acid as a chelating ligand and catalyzer. Single layer and multi-layered Ta₂O₅ films were fabricated at a dipping rate of 107 mm/min. The microstructure, stoichiometry and optical properties of these films were investigated as a function of the film thickness. Room temperature CV measurements clearly revealed a protonic conductor behavior for Ta₂O₅ films. Optical properties such as refractive index, extinction coefficient and optical band gap value of the Ta₂O₅ films were calculated from optical transmittance measurements. It was found that the refractive index and extinction coefficient values were affected by the thickness of the coatings. The refractive index at a wavelength of 550 nm increased from 1.70 to 1.72 with increasing film thickness. The optical band gap value (3.75±0.12 eV) of the coating was unaffected by the film thickness. These results indicate that sol–gel-deposited Ta₂O₅ films have a promising application as proton conductors in electrochromic devices.