Forecasting Practices in Textile and Apparel Export Industry a Systematic Review

Sustainability in textile and apparel is an ideal that requires organizational effort starting from eco-design, encompassing manufacturing, distribution, and consumption. However, in the circular economy, the idea further goes to reuse the raw material. Sustainability is still an evolving subject in apparel and textile, which needs to investigate from many angles. Excess inventory at the supplier's end also impacts sustainability and needs due attention from researchers and practitioners to ponder. Applying the correct forecast technique and minimum errors results in better financial performance and reduced environmental pollution, impacting the triple bottom line in the true sense. The current study uses a systematic review on textile and apparel forecasting, highlighting the earlier research, thus contributing to the literature on sustainability and supply chain management.

Evaluation of the performance of tropical cyclone track prediction techniques

ABSTRACT. This paper reviews the methods by which techniques for predicting tropical cyclone (TC) motion can be evaluated. Different error measures (forecast error, systematic error, and cross-track and along-track errors) are described in detail. Examples are then given to show how these techniques can be further evaluated by stratifying the forecasts based on factors related to the TC, including latitude, longitude, intensity change, size and past movement. Application of the Empirical-Orthogonal-Function (EOF) approach to represent the environmental flow associated with the TCs is also proposed. The magnitudes of the EOF coefficients can then be used to stratify the forecasts since these coefficients represent different types of flow fields. A complete evaluation of a forecast technique then consists of a combination of analyzing the different error measures based on both the storm- related factors and the EOF coefficients.

Slice forecast and its accuracy criterion

Subject. This article provides a mathematical formulation of a slice-based forecast technique allowing a comprehensive assessment of future changes in the dynamics and structure of economic systems. The technique is based on an analysis and integration of a set of time series of heterogeneous indicators combined in a system logical algorithm of information synthesis called a slice. A slice forecast accuracy criterion is proposed as well. Objectives. Slice forecasts are designed to improve the quality and efficiency of economic forecasts. Methods. The slice forecast technique is based on a slice technology as a set of methods to collect, process, analyze, and synthesize information and knowledge. Results. The article presents a calculation based on eight series of macroeconomic indicators that characterize the development of the economy of the Russian Federation for the period from 2000 to 2021. It shows new possibilities of analysis and description of economic systems, cycles and crisis phenomena. Conclusions. The results obtained show that the slice technique helps solve a number of urgent problems to improve the quality of foreseeing future changes.

NEXUS BETWEEN FINANCIAL INNOVATION AND BANKRUPTCY: EVIDENCE FROM INFORMATION, COMMUNICATION AND TECHNOLOGY (ICT) SECTOR

This study examines the reliability of Altman’s [Formula: see text]-score model to predict the financial failure of the ICT sector in Pakistan. Data for 11 PSE-listed (Pakistan Stock Exchange) ICT companies were collected through Altman’s [Formula: see text]-score model in the period 2013–2018. The innovative Altman [Formula: see text]-bankruptcy forecast technique has been used for the analysis. Results show that the four companies, Pakistan International Airlines Corp. Ltd., TRG Pakistan Ltd., World call Telecom Ltd. and Media Times Ltd. were in the distress zone; Pakistan Telecommunication Co. Ltd. was in the gray zone; and the remaining six companies (i.e., Hum Network Ltd., Nestle Technologies Ltd., Pakistan Int., Container Terminal Ltd., Pak Datacom Ltd., Pakistan National Shipping Corp. and Tele card Ltd.) were able to meet the safe zone criteria. On the other hand, the [Formula: see text]-score analysis suggests that seven ICT companies would not go bankrupt, while the remaining four companies failed financially and would not maintain businesses in the future. Furthermore, according to the innovative outcome analysis, X3-EBIT/TA has a significant positive relationship with X1-WC/TA and X4-TE/TL and X2-RETA has a significant positive association with X1-WC/TA and a negative association with X4-TE/TL.

Forecasting Tourist Daily Arrivals With A Hybrid Sarima–Lstm Approach

Timely predicting tourist demand is extremely important for the tourism industry. However, due to limited availability of data, most of the relevant research studies have focused on data on a quarterly or monthly basis. In this article, we propose a novel hybrid approach, SARIMA + LSTM, that is, seasonal autoregressive integrated moving average (SARIMA) combined with long short-term memory (LSTM) to forecast daily tourist arrivals to Macau SAR, China. The LSTM model is a novel artificial intelligence nonlinear method which has been shown to have the capacity to learn the long-term dependencies existing in the time series. SARIMA + LSTM benefits from the predictive power of the SARIMA model and the ability of the LSTM to further reduce residuals. The results show that the SARIMA + LSTM forecast technique outperforms other methods.

Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation

We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

Assessment of the Sales Forecast Technique Double-Weighted Moving Average vs Other Widely Used Forecasting Techniques

In order to improve the operations planning of two companies, whose main business is to be chemical products suppliers in Mexico, it was made the sales forecast of a fourth year of operations, using the monthly sales data information of the three previous years. The objective of the chemical suppliers forecast was to be in a better position to satisfy the multiple and varied needs of their clients, which demand different quantities of products and have different consumption patterns. The sales forecast was made by the next six techniques: Simple Moving Average (SMA), Weighted Moving Average (WMA), Trend Projection (TP), Exponential Smoothing (ES), Simple Linear Regression (SLR), and the recently proposed (Castillo, et al. 2016) technique called: Double-Weighted Moving Average (DWMA). The three years monthly sales data of 61 products, handled by the two companies, were processed in order to obtain the monthly forecast of the fourth year. After the fourth year, the forecasted data were compared with real monthly sales data. The analysis was made by the determination of the Symmetric Mean Absolute Percentage Error (SMAPE), which gave the next results: In the case of company 1, the average errors for the five reference techniques (SMA, WMA, TP, ES and SLR) was in the range 0.235 – 0.351] vs 0.249 for the DWMA. For company 2, the average error, of the same five reference techniques was in the range [0.292 – 0.467] vs 0.282 for the DWMA. WMA was the second technique in giving the least forecasting errors. In both companies, DWMA was the forecasting technique with one of the lowest average error and the lowest error in most of the products.

Prediction of Cell Phone Client’s Location utilizing Semantic Trajectory

Investigation on anticipating developments of cell phone consumers has pulled in a great deal of considerations lately. Considerable foreseeing procedures are created dependent on geographic zonal highlights of cell phone abuser’s directions. In this research, we put forward a new methodology for anticipating the subsequent place of a client's development dependent in cooperation of the territory and semantic highlights of clients' directions. Center thought of the expectation structure depends on new cluster centered forecast technique it assesses the subsequent place a cell phone client dependent on the continuous practices of comparable clients in analogous group controlled by dissecting clients' normal conduct in semantic directions. Through an exhaustive assessment by tests, our proposition is appeared to convey fantastic execution.

Rain Attenuation Predicted Model for 5G Communication in Tropical Regions

The signals operating at higher microwave frequency ranges get attenuated in the tropical regions where heavy rainfall occurs. Controlling of Signal fading for establishment of efficient link plays an important role in the heavy rainfall regions. Here rain attenuation predicted model has been designed in sub- 6 GHz and mm Wave bands. This predicted model is applicable to the tropical regions where heave rainfall occurs. Frequency variation technique has been adopted to execute the research work. The estimated rain attenuation depends on International Telecommunication UnionR rain mitigation forecast technique utilizing assessment of rain in the tropical regions of South East Asia.The frequency ranges used here for variation techniques are respectively 3.6 to 4.2 GHz, 4.4 to 4.9 GHz, 27.5 to29.5 GHz, 37 to-40 GHz and 64 to71 GHz. In the previous works [1] it is observed that only lower fade margin has been considered for communication link design .As the fade margin increases, the communication link seems to be more reliable. In this paper the fade margin has been increased and it has been chosen from 12dB to 16dB. This predicted model will yield better result than that of ITU-R model.

Auto-Scaling Provision Basing on Workload Prediction in the Virtualized Data Center

With the development in the Cloud datacenters, the purpose of the efficient resource allocation is to meet the demand of the users instantly with the minimum rent cost. Thus, the elastic resource allocation strategy is usually combined with the prediction technology. This article proposes a novel predict method combination forecast technique, including both exponential smoothing (ES) and auto-regressive and polynomial fitting (PF) model. The aim of combination prediction is to achieve an efficient forecast technique according to the periodic and random feature of the workload and meet the application service level agreement (SLA) with the minimum cost. Moreover, the ES prediction with PSO algorithm gives a fine-grained scaling up and down the resources combining the heuristic algorithm in the future. APWP would solve the periodical or hybrid fluctuation of the workload in the cloud data centers. Finally, experiments improve that the combined prediction model meets the SLA with the better precision accuracy with the minimum renting cost.