scholarly journals Deep Learning Based Customer Preferences Analysis in Industry 4.0 Environment

Author(s):  
Qindong Sun ◽  
Xingyu Feng ◽  
Shanshan Zhao ◽  
Han Cao ◽  
Shancang Li ◽  
...  

AbstractCustomer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation.

Author(s):  
Alok Yadav ◽  
Sajal Ghosh

Because of long product development cycles, effective production planning of automobiles requires accurate demand forecasting in order to effectively managing resources and maximizing revenue. Errors in demand forecasts have often led to enormous costs and loss of revenue due to suboptimal utilization of resources. Since early 2000 India has been the largest manufacturer and consumer of farm tractors in the world. This paper develops multiplicative seasonal autoregressive integrated moving average (MSARIMA) and autoregressive moving average model with exogenous variable (ARMAX) to forecast monthly demand for farm tractor. The result indicates that ARMAX with real agriculture credit has found to be outperformed MSARIMA model in forecasting demand of farm tractors in the horizon of six months. The accurate monthly forecasting of farm tractor would help the manufacturers for better raw material, inventory and supply chain management. Keywords


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.


2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.


J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.


2013 ◽  
Vol 114 (10) ◽  
pp. 1406-1412 ◽  
Author(s):  
Angela S. M. Salinet ◽  
Thompson G. Robinson ◽  
Ronney B. Panerai

The association between neural activity and cerebral blood flow (CBF) has been used to assess neurovascular coupling (NVC) in health and diseases states, but little attention has been given to the contribution of simultaneous changes in peripheral covariates. We used an innovative approach to assess the contributions of arterial blood pressure (BP), PaCO2, and the stimulus itself to changes in CBF velocities (CBFv) during active (MA), passive (MP), and motor imagery (MI) paradigms. Continuous recordings of CBFv, beat-to-beat BP, heart rate, and breath-by-breath end-tidal CO2 (EtCO2) were performed in 17 right-handed subjects before, during, and after motor-cognitive paradigms performed with the right arm. A multivariate autoregressive-moving average model was used to calculate the separate contributions of BP, EtCO2, and the neural activation stimulus (represented by a metronome on-off signal) to the CBFv response during paradigms. Differences were found in the bilateral CBFv responses to MI compared with MA and MP, due to the contributions of stimulation ( P < 0.05). BP was the dominant contributor to the initial peaked CBFv response in all paradigms with no significant differences between paradigms, while the contribution of the stimulus explained the plateau phase and extended duration of the CBFv responses. Separating the neural activation contribution from the influences of other covariates, it was possible to detect differences between three paradigms often used to assess disease-related NVC. Apparently similar CBFv responses to different motor-cognitive paradigms can be misleading due to the contributions from peripheral covariates and could lead to inaccurate assessment of NVC, particularly during MI.


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