Fashion Color Forecasting by Applying an Improved Back Propagation Neural Network

Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.

Electric Load Forecasting for Shanghai Urban Area

In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis of the data during 1992～1998, and the approximate load in 2010 is forecast on the basis of the data during 1990～1998.

Research on trend prediction of component stock in fuzzy time series based on deep forest

With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry.To improve the effectiveness of stock trend prediction and solve the problems in time series data processing,this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index.Meanwhile,in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge,this paper combines the deep forest model to train the stock data separately.The experimental results show that(1)the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit(GRU)model when the un-fuzzy index-adjusted dataset is used as features for input,(2)the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input,(3)the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and(4)the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.

Cotton Incorporated:Color & Surface——Trend Forecast S/S 2011

Daredevil·Gladiator·Cherry Red·Whirlwind·Flash·Grit·Spider Grass·MercuryWith less to lose,we take a risk,creating challenges and pushingourselves in ways we only fantasized about while living in the safezone.Self-tested,defying limits,we feel invigorated.Each stepwe take towards our wildest dreams brings us one step closer tothe edge.This dynamic palette merges bold and muted tones.Itschameleon nature applies from active to formal wear.

SSI9 Trend forecast

Inquisitor Making sense of the static around us,digging deeper permeates every aspect of our lives.Scrutinizing instead of assuming,we are inspired to self-educate which infiltrates everything from our consumer decisions to global perspectives.Self-identification through brands or political movements loses steam as perspective is gained through more unbiased or raw outlets.

Forecast Forecasts the Trend

The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

Spatiotemporal differentiation of coupling and coordination relationship of production-living-ecological space in Yellow River Basin

Based on combing the existing research on the production-living-ecological space,the paper quantitatively analyzes the evaluation system-development level-temporal and spatial evolution,future trend-influencing factors of the production-living-ecological functions coupling and coordination in the Yellow River Basin from 2009 to 2018.Through multi-scale analysis and comparison,the paper tries to identify problem areas and put forward corresponding measures.The research results show that:(1)The coupling and coordination degree of the production-living-ecological functions in the Yellow River Basin both show an upward trend,and its future growth trend is relatively slow.(2)The spatial-temporal differential characteristics of the coordinated development level of the production-living-ecological functions are obvious,and gradually develop towards the direction of benign resonance in time,showing a spatial distribution pattern of“high in the northeast and low in the southwest”.(3)There is a big difference in the level of coordinated development of the production-living-ecological functions,and the coordination degree of the“production-living”function is the lowest.(4)Scientific and technological investment,economic development level,government capacity,and urbanization level have a significant positive impact on the spatial effect of the coordinated development of the production-living-ecological functions of the Yellow River Basin,and the same factor has different effects on different regions.

“Towards Re-Inventing Psychohistory”:Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.