Power Customer Complaint Prediction Model Based on Time Series Analysis

To improve customer service of power enterprises, this paper constructs an intelligent prediction model for customer complaints in the near future based on the big data on power service. Firstly, three customer complaint prediction models were established, separately based on autoregressive integrated moving average (ARIMA) time series algorithm, multiple linear regression (MLR) algorithm, and backpropagation neural network (BPNN) algorithm. The predicted values of the three models were compared with the real values. Through the comparison, the BPNN model was found to achieve the best predictive effect. To help the BPNN avoid local minimum, the genetic algorithm (GA) was introduced to optimize the BPNN model. Finally, several experiments were conducted to verify the effect of the optimized model. The results show that the relative error of the optimized model was less than 40% in most cases. The proposed model can greatly improve the customer service of power enterprises.

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On the Prediction of Upwelling Events at the Colombian Caribbean Coasts from Modis-SST Imagery

Sensors ◽

10.3390/s19132861 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2861 ◽

Cited By ~ 1

Author(s):

José J. Alonso del Rosario ◽

Juan M. Vidal Pérez ◽

Elizabeth Blázquez Gómez

Keyword(s):

Time Series ◽

Prediction Model ◽

Prediction Models ◽

Moving Average ◽

Autoregressive Moving Average ◽

Maximum Entropy Spectral Analysis ◽

Nodal Lines ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Karhunen Loeve Transform

The upwelling cores on the Caribbean Colombian coasts are mainly located at the Peninsula de la Guajira and Cabo de la Aguja. We used monthly averaged Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature as the only information to build up a prediction model for the upwelling events. This comprised two steps: (i) the reduction of the complexity by means of the Karhunen–Loève transform and (ii) a prediction model of time series. Two prediction models were considered: (a) a parametric autoregressive-moving average (ARMA) time series from the Box–Jenkins methodology and (b) a harmonic synthesis model. The harmonic synthesis also comprised of two steps: the maximum entropy spectral analysis and a least-squares harmonic analysis on the set of frequencies. The parametric ARMA time series model failed at the time of prediction with a very narrow range, and it was quite difficult to apply. The harmonic synthesis allowed prediction with a horizon of six months with a correlation of about 0.80. The results can be summarized using the time series of the weights of the different oscillation modes, their spatial structures with the nodal lines, and a high confidence model with a horizon of prediction of about four months.

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Customer complaints and service policy in electronic commerce

South African Journal of Business Management ◽

10.4102/sajbm.v44i3.158 ◽

2013 ◽

Vol 44 (3) ◽

pp. 15-20 ◽

Cited By ~ 3

Author(s):

Y-W. Fan ◽

Y-F. Miao ◽

S-C. Wu

Keyword(s):

Electronic Commerce ◽

Customer Service ◽

Service Failure ◽

Business Managers ◽

Customer Complaints ◽

Prevention Policies ◽

Customer Complaint ◽

Service Policy ◽

Number Of Customers

Handling customer complaints is an important strategy to retain customers. Therefore, in the event of service failure, e-retailers should concentrate on recovery policies. However, studies discussing prevention policies to avoid customer complaints are scant. This study collected 5933 real customer complaint data from an electronic commerce customer-service database and classified customer complaints into 6 types. The findings showed that a number of customers were dishonest and took advantage of recovery policies. After interviewing business managers and consultants, this research suggests that e-retailers have prevention policies to guarantee accuracy of packaging and delivery processes. Prevention policies can reduce customer complaints, and avoid extra costs for businesses conducting recovery policies.

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Grey system based novel approach for stock market forecasting

Grey Systems Theory and Application ◽

10.1108/gs-04-2015-0014 ◽

2015 ◽

Vol 5 (2) ◽

pp. 178-193 ◽

Cited By ~ 10

Author(s):

R.M. Kapila Tharanga Rathnayaka ◽

D.M.K.N Seneviratna ◽

Wei Jianguo

Keyword(s):

Time Series ◽

Prediction Models ◽

Stock Exchange ◽

Moving Average ◽

Grey Prediction ◽

Grey System ◽

Short Term ◽

Content Type ◽

Stock Market Forecasting ◽

Novel Approach

Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

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Prognosis of Chronic Renal Syndrome by Classification and Progression Using Temporal Abstraction

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9927.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 53-58

Keyword(s):

Genetic Algorithm ◽

Time Series ◽

Renal Disease ◽

Prediction Model ◽

Prediction Models ◽

Series Data ◽

Support Vector ◽

Temporal Abstraction ◽

Graphic Data ◽

Lab Test

Chronic renal syndrome is defined as a progressive loss of renal function over period. Analysers have make effort in attempting to diagnosis the risk factors that may affect the retrogression of chronic renal syndrome. The motivation of this project helps to develop a prediction model for level 4 CKD patients to detect on condition that, their estimated Glomerular Filtration Rate (eGFR) stage downscale to lower than 15 ml/min/1.73 m². End phase renal disease, after six months accumulating their concluding lab test observation by assessing time affiliated aspects. Data mining algorithm along with Temporal Abstraction (TA) are confederated to reinforce CKD evolvement of prognostication models. In this work a inclusive of 112 chronic renal disease patients are composed from April 1952 to September 2011 which were extracted from the patient’s Electronic Medical Records (EMR). The information of chronic renal patients are collected in a big spatial info-graphic data. In order to analyse these info-graphic data, it is significant to detect the issues affecting CKD deterioration and hence it becomes a challenging task. To overcome this challenge, time series graph has been generated in this project work based on creatinine and albumin lab test values and reports of the time period. The presence of CKD diagnostic codes are transformed into default seven digit default format of International Classification of Disease 10 Clinical Modification (ICD 10 CM). Feature selection is performed in this work based on wrapper method using genetic algorithm. It is helpful for finding the most relevant variables for a predictive model. High Utility Sequential Rule Miner (HUSRM) is used here to address the discovery of CKD sequential rules based on sequence patterns. Temporal Abstraction (TA) techniques namely basic TA and complex TA are used in this work to analyse the status of chronic renal syndrome patients. Classification and Regression Technique (CART) along with Adaptive Boosting (AdaBoost) and Support Vector Machine Boosting (SVMBoost) are applied to develop the CKD in which the progression prediction models exhibit most accurate prediction. The results obtained from this work divulged that comprehending temporal observation forward the prognostic instances has escalated the efficacy of the instances. Finally, an evaluation metrics namely accuracy, sensitivity, specificity, positive likelihood, negative likelihood and Area Under the Curve (AUC) are helps to evaluate the performance of the prediction models which are designed and implemented in this project. Key Words: CKD, progression, time series data, genetic algorithm, sequential rules, TA classification and prediction model.

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The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽

10.1155/2021/6663773 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Tongfei Lao ◽

Xiaoting Chen ◽

Jianian Zhu

Keyword(s):

Time Series ◽

Prediction Model ◽

Prediction Accuracy ◽

Prediction Models ◽

Small Sample ◽

Grey Prediction ◽

Dynamic Background ◽

Background Values ◽

Grey Prediction Model ◽

Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

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Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

Entropy ◽

10.3390/e23101305 ◽

2021 ◽

Vol 23 (10) ◽

pp. 1305

Author(s):

Shenghan Zhou ◽

Bang Chen ◽

Houxiang Liu ◽

Xinpeng Ji ◽

Chaofan Wei ◽

...

Keyword(s):

Time Series ◽

Prediction Model ◽

Moving Average ◽

Traffic Prediction ◽

Data Sets ◽

Prediction Modeling ◽

Autoregressive Integrated Moving Average ◽

Smart Transportation ◽

Characteristics Analysis ◽

Operational Data

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.

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Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/1720134 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Xinli Zhang ◽

Yu Yu ◽

Fei Xiong ◽

Le Luo

Keyword(s):

Time Series ◽

Prediction Model ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Blood Sampling ◽

Series Data ◽

Large Hospital ◽

Daily Number ◽

Day Of The Week

This paper is aimed at establishing a combined prediction model to predict the demand for medical care in terms of daily visits in an outpatient blood sampling room, which provides a basis for rational arrangement of human resources and planning. On the basis of analyzing the comprehensive characteristics of the randomness, periodicity, trend, and day-of-the-week effects of the daily number of blood collections in the hospital, we firstly established an autoregressive integrated moving average model (ARIMA) model to capture the periodicity, volatility, and trend, and secondly, we constructed a simple exponential smoothing (SES) model considering the day-of-the-week effect. Finally, a combined prediction model of the residual correction is established based on the prediction results of the two models. The models are applied to data from 60 weeks of daily visits in the outpatient blood sampling room of a large hospital in Chengdu, for forecasting the daily number of blood collections about 1 week ahead. The result shows that the MAPE of the combined model is the smallest overall, of which the improvement during the weekend is obvious, indicating that the prediction error of extreme value is significantly reduced. The ARIMA model can extract the seasonal and nonseasonal components of the time series, and the SES model can capture the overall trend and the influence of regular changes in the time series, while the combined prediction model, taking into account the comprehensive characteristics of the time series data, has better fitting prediction accuracy than a single model. The new model can well realize the short-to-medium-term prediction of the daily number of blood collections one week in advance.

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How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

Infrastructures ◽

10.3390/infrastructures6020028 ◽

2021 ◽

Vol 6 (2) ◽

pp. 28

Author(s):

Seyed Amirhossein Hosseini ◽

Omar Smadi

Keyword(s):

Decision Making ◽

Prediction Model ◽

Prediction Models ◽

Reduction Rate ◽

Pavement Management ◽

Decision Making Process ◽

Pavement Management Systems ◽

Maintenance And Rehabilitation ◽

Predicted Values ◽

The Cost

One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. The cost of treatment (in millions of dollars) over 20 years for five different scenarios increased from ($54.07–$92.95), ($53.89–$155.48), and ($74.41–$107.77) for asphalt, composite, and concrete pavement types, respectively. Increasing the rate of error also contributed to the prediction model, resulting in a higher benefit reduction rate.

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PREDIKSI RETAIL SALES MOBIL TOYOTA MENGGUNAKAN METODE ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE)

Indonesian Journal of Strategic Management ◽

10.25134/ijsm.v3i1.2817 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Adri Arisena

Keyword(s):

Time Series ◽

Prediction Model ◽

Moving Average ◽

Retail Sales ◽

Autoregressive Integrated Moving Average ◽

Automobile Manufacturing ◽

Automobile Factory ◽

Sales Prediction ◽

Car Sales

AbstrakPenjualan mobil di Indonesia saat ini terus meningkat. Untuk melakukan perencanaan yang baik, perusahaan membutuhkan sebuah nilai prediksi penjualan agar dapat menentukan target penjualan mobil. Salah satu model prediksi yang sering digunakan untuk memprediksi sebuah data yaitu metode ARIMA (Autoregressive Integrated Moving Average). Dalam penelitian ini data yang digunakan yaitu data Retail Sales mobil Toyota dari bulan Januari 2017 sampai dengan Maret 2020. Retail Sales merupakan penjualan mobil dari dealer kepada konsumen sehingga diharapkan pabrik mobil Toyota memiliki prencanaan terhadap produksi mobil sehingga menjadi efektif dan efisien.Kata Kunci : ARIMA, time series, Toyota, MobilAbstrackCar sales in Indonesia are growing. To do good planning, the company needs a sales prediction value in order to determine the car sales target. One prediction model that is often used to predict a data is the method of ARIMA (Autoregressive Integrated Moving Average). In this study the data used is the Toyota Sales Retail data from January 2017 to March 2020. Retail Sales is a car sales from dealers to consumers, so it is hoped that the Toyota automobile factory has a preproduction to the automobile manufacturing so that it becomes effective and efficient.Keywords : ARIMA, time series, Toyota, Car

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LEARNING A COMBINED MODEL OF TIME SERIES FORECASTING

Ukrainian Journal of Information Technology ◽

10.23939/ujit2021.03.044 ◽

2021 ◽

Vol 3 (1) ◽

pp. 44-48

Author(s):

F. E. Geche ◽

◽

O. Yu. Mulesa ◽

A. Ye. Batyuk ◽

V. Yu. Smolanka ◽

...

Keyword(s):

Time Series ◽

Prediction Models ◽

Weighting Factor ◽

Combined Model ◽

Forecasting Accuracy ◽

Basic Model ◽

Time Period ◽

Optimal Value ◽

Predicted Values ◽

Combined Forecasting

The method of construction of the combined model of forecast ing of time series based on basic models of forecasting is developed in the work. The set of basic models is dynamic, ie new prediction models can be included in this set. Models also can be deleted depending on the properties of the time series. For the synthesis of a combined model of forecasting time series with a given forecast step, the optimal step of prehistory is determined at the beginning. Next the functional is constructed. The optimal prehistory step is determined using the autoregression method for a fixed forecast step. It determines the period of time at which the accuracy of models from the base set is analyzed. For each basic model during the process of the construction of the combined model is determined by the weighting factor with which it will be included in the combined model. The weights of the basic models are determined based on their forecasting accuracy for the time period determined by the prehistory step. The weights reflect the degree of influence of the base models on the accuracy of the combined model forecasting. After construction of the combined model, its training is carried out and those basic models which will be included in the final combined model of forecasting are defined. The rule of inclusion of basic models in the combined model is established. While including basic models in the combined forecasting model, their weights are taken into account, which depends on the same parameter. The optimal value of the parameter is determined by minimizing the given functional, which sets the standard deviation between the actual and predicted values of the time series. Weights with optimal parameters are ranked in decreasing order and are used to include basic models in the combined model. As a result of this approach, as predicted values for the real time series show, it was possible to significantly improve the forecasting accuracy of the combined model in many cases. The developed method of training provides the flexibility of the combined model and its application to a wide class of time series. The results obtained in this work contribute to solving the problem of choosing the most effective basic models by synthesizing them into one combined model.

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