scholarly journals PENGGUNAAN KAIN TROSO DAN APLIKASI MAKRAME PADA BUSANA READY TO WEAR

Texere ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 71-82
Author(s):  
Tina Martina ◽  
Ursae Pramesvari ◽  
Santi Ramadhanti
Keyword(s):  
Trend Forecasting

Indonesia memiliki kekayaan ragam kain tenun  tradisional dari masing-masing daerah. Salah  satu  daerah  yang menghasilkan  kain  tenun  tradisional  yaitu kabupaten Jepara, Jawa  Tengah.  Kain  tenun  yang diproduksi  di  desa  Troso  Kabupaten  Jepara  disebut  dengan  kain  tenun  Troso. Dalam upaya memperkenalkan kain tenun Troso, maka disusun rencana penelitian ini yang bertujuan  untuk memberikan  alternatif  baru  penggunaan  kain  tradisional  tenun  ikat  troso  dan aplikasi teknik ikat mengikat atau lebih dikenal dengan makrame yang pembuatannya dilakukan secara manual sebagai nilai  tambah dalam pembuatan busana. Hasil dari  penelitian  ini  berupa  busana  ready to wear  yang  terinspirasi  dari  Trend Forecasting Fashion tahun 2019-2020 Singularity yang diterbitkan oleh Bekraf dengan tema Svarga. Metodologi yang digunakan adalah studi literatur dan studi lapangan dalam proses eksperimen pembuatan  produk.  Kain  tenun  Troso  dan  kain  tambahan  yang  akan  digunakan memerlukan sifat yang menghasilkan kenyamanan serta kekuatan yang baik bagi penggunanya.  Hal  tersebut  mendasari  perlunya  dilakukan pengujian untuk kain tenun troso dan kain pendukung. Selain itu aplikasi makrame yang diterapkan akan diproduksi dengan teknik  pencelupan  zat warna  untuk memperoleh  hasil  gradasi warna. Produk  tekstil  atau  fashion  tersebut  kemudian  dinilai  secara  ekonomi  di masyarakat  dengan menggunakan metode kuantitatif berupa kuisioner.

scholarly journals Knowledge Enhanced Neural Fashion Trend Forecasting

2020 ◽  
Author(s):  
Yunshan Ma ◽  
Yujuan Ding ◽  
Xun Yang ◽  
Lizi Liao ◽  
Wai Keung Wong ◽  
...  
Keyword(s):  
Trend Forecasting

A novel cryptocurrency price trend forecasting model based on LightGBM

2020 ◽  
Vol 32 ◽  
pp. 101084 ◽  
Author(s):  
Xiaolei Sun ◽  
Mingxi Liu ◽  
Zeqian Sima
Keyword(s):  
Forecasting Model ◽  
Price Trend ◽  
Model Based ◽  
Trend Forecasting

Value added analysis and trend forecasting in the manufacturing industry in Kaliningrad oblast

2017 ◽  
Vol 28 (2) ◽  
pp. 160-168 ◽  
Author(s):  
N. A. Kazakova ◽  
A. I. Bolvacheva ◽  
A. L. Gendon ◽  
G. F. Golubeva
Keyword(s):  
Manufacturing Industry ◽  
Value Added ◽  
Kaliningrad Oblast ◽  
Trend Forecasting

scholarly journals REGRESSION ANALYSIS APPLICATION FOR TELETRAFFIC THEORY TASKS

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 18-25
Author(s):  
Alina A. Sherstneva ◽  
Keyword(s):  
Regression Analysis ◽  
Least Squares ◽  
Experimental Analysis ◽  
Random Sequence ◽  
Measurement Data ◽  
Real Data ◽  
Polynomial Model ◽  
Theoretical Research ◽  
Graphical Form ◽  
Trend Forecasting

The article aims to consider least squares approach for solving problems of queuing systems theory. The opportunity of predicting the behavior of infocommunication system is shown. Choosing the optimal model of its functioning is proposed. On base monitoring system metrics, statistical data were formed. The article proposes to make data trend forecasting, to estimate parameters of random processes over time. To obtain the results of functioning data in infocommunication systems that are as close as possible to the real values, polynomial and sine models are considered. The method of regression analysis is proposed to determine the parameter values for a model from a set of observational data. In theoretical research, the linear and nonlinear least squares methods are used in terms of a circle. The task of experimental analysis is to obtain an estimated parameter of sine, polynomial models and the center of circle. Experimental analysis was performed using the mathematical modeling program Matlab. A uniformly distributed random sequence and a random sequence with normal distribution are generated. The sequence with experimental data for polynomial and sine models, respectively, are calculated. The correspondence each model for generated data is shown in graphical form. The measurement data obeys observations. The estimated parameters are summarized in the tables. The polynomial order is estimated. The estimated dispersion curve of the polynomial model is obtained. The calculated variance values of the polynomial model are presented. Data trend forecasting for measurement data is made. The estimated values are extremally close to real data. The results are shown in graphs. Finally, an approximate model of the circumference of measurement data is presented in graphical form. After some iterations with estimated center from the arithmetic mean the new circle center is given. And quite close values for center and radius of circle are obtained.

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

Cool Hunting

On Trend ◽  
2019 ◽  
pp. 62-80
Author(s):  
Devon Powers
Keyword(s):  
Young People ◽  
Close Attention ◽  
New Yorker ◽  
The World ◽  
Trend Forecasting

This chapter looks at “cool hunting,” the brand of trend forecasting that took root around the world during the 1990s and 2000s. Companies of the era were becoming increasingly obsessed with understanding youth trends, thereby inspiring a fleet of upstart advisory companies spearheaded by young people. The chapter discusses how these services developed and popularized and pays close attention to the role of Malcolm Gladwell, whose 1997 New Yorker article “The Coolhunt” named and rapidly spread these practices.

Sign in / Sign up

Export Citation Format

Share Document