Clustering-Based Stability and Seasonality Analysis for Optimal Inventory Prediction

2014 ◽  
pp. 1-18
Author(s):  
Manish Joshi ◽  
Pawan Lingras ◽  
Gajendra Wani ◽  
Peng Zhang
Keyword(s):  
Time Series ◽  
Inventory Management ◽  
Moving Average ◽  
Time Series Prediction ◽  
Real Life ◽  
Chain Stores ◽  
Retail Chain ◽  
System A ◽  
Versatile Tool ◽  
Prediction Techniques

This chapter exemplifies how clustering can be a versatile tool in real life applications. Optimal inventory prediction is one of the important issues faced by owners of retail chain stores. Researchers have made several attempts to develop a generic forecasting model for accurate inventory prediction for all products. Regression analysis, neural networks, exponential smoothing, and Autoregressive Integrated Moving Average (ARIMA) are some of the widely used time series prediction techniques in inventory management. However, such generic models have limitations. The authors propose an approach that uses time series clustering and time series prediction techniques to forecast future demand for each product in an inventory management system. A stability and seasonality analysis of the time series is proposed to identify groups of products (local groups) exhibiting similar sales patterns. The details of the experimental techniques and results for obtaining optimal inventory predictions are shared in this chapter.

scholarly journals A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques

2015 ◽  
Vol 11 (1) ◽  
pp. 110-123
Author(s):  
Khadeega Abd Al-zahra ◽  
Khulood Moosa ◽  
Basil Jasim
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Time Series Prediction ◽  
Critical Issue ◽  
Prediction System ◽  
System A ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Electrical Demand ◽  
Electrical Consumption

The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models (ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated moving average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro-Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared with the traditional ARIMA method.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

Hybrid Neural Models For Rice Yields Times Forecasting

2012 ◽  
Author(s):  
Ruhaidah Samsudin ◽  
Puteh Saad ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Data Sets ◽  
Ann Model ◽  
Yield Data ◽  
Rice Yields ◽  
Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

A Decision Support Tool (DST) for Inventory Management

2019 ◽  
Vol 11 (2) ◽  
pp. 27-47
Author(s):  
Okure Udo Obot ◽  
Uduak David George ◽  
Victoria Sunday Umana
Keyword(s):  
Decision Support ◽  
Inventory Management ◽  
Moving Average ◽  
Real Life ◽  
Decision Support Tool ◽  
Unit Price ◽  
Public Power ◽  
Support Tool ◽  
Eoq Model ◽  
Real Life Situation

Loss of customer goodwill is one of the greatest losses a business organization can incur. One reason for such a loss is stock outage. In an attempt to solve this problem, an overstock could result. Overstock comes with an increase in the holding and carrying cost. It is an attempt to solve these twin problems that an economic order quantity (EOQ) model was developed. Information on fifteen items comprised of 10 non-seasonal and 5 seasonal items was collected from a supermarket in Ikot Ekpene town, Nigeria. The information includes the quantity of daily sales, the unit price, the lead time and the number of times an item is ordered in a month. Based on this information, a simple moving average and y-trend method of forecasting were used to forecast the sales quantity for the following month for the non-seasonal and seasonal items. The forecast value was used to compute the EOQ for each of the items. Different scenarios were created to simulate the fuzzy logic EOQ after which the result of the conventional method, EOQ method, and fuzzy EOQ methods were obtained and compared. It was revealed that if the EOQ method is adopted, savings of 43% of holding and carrying cost would be made. From the scenarios of a fuzzy EOQ, a savings of 35.65% was recorded. It was however observed that in a real-life situation, the savings on a fuzzy EOQ is likely to be higher than that of an EOQ considering the incessant public power outages and the increase in transportation fares due to the high cost of fuel and the bad state of roads in Nigeria. To this end, a Decision Support Tool (DST) was developed to help the supermarket manage its inventory based on daily predictions. The DST incorporates a filter engine to take care of some emotional and cognitive incidences within the environment.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

Time Series Prediction Techniques for Estimating Remaining Useful Lifetime of Cutting Tool Failure

2014 ◽  
Vol 9 (10) ◽  
pp. 1783 ◽  
Author(s):  
Siti Azirah Asmai ◽  
Burairah Hussin ◽  
Mokhtar Mohd Yusof ◽  
Abdul Samad Shibghatullah
Keyword(s):  
Time Series ◽  
Cutting Tool ◽  
Time Series Prediction ◽  
Tool Failure ◽  
Prediction Techniques ◽  
Useful Lifetime

scholarly journals Epidemiological Prognosis of Pertussis Incidence in Bulgaria

Folia Medica ◽  
2020 ◽  
Vol 62 (3) ◽  
pp. 509-514
Author(s):  
Ralitsa Raycheva ◽  
Yordanka Stoilova ◽  
Ani Kevorkyan ◽  
Vanya Rangelova
Keyword(s):  
Time Series ◽  
Health Management ◽  
Whooping Cough ◽  
Moving Average ◽  
Real Life ◽  
Acceptable Error ◽  
Autoregressive Integrated Moving Average ◽  
One Step ◽  
Original Time ◽  
Near Future

Introduction: Epidemiological forecasting facilitates scientifically sound solutions to upcoming theoretical and practical issues, in the development of public health management, in particular of infectious diseases. Aim: To critically analyze the most recent scientific advances in the biosocial nature and methodology of epidemiological forecasting to present a real-life example of pertussis, a disease with shifting epidemiology. Materials and methods: For the prediction of pertussis morbidity the autoregressive integrated moving average (ARIMA) the model was established by utilizing the method of time series analysis to construct a model of overall morbidity using Time series modeller in SPSS v.25. To model pertussis morbidity we obtained official data from the Ministry of Health and the National Center for Infectious and Parasitic Diseases, since the beginning of disease registration from 1903 until 2018. We also analyzed the shifting epidemiology of pertussis. Results: The proper identification procedures we applied indicated ARIMA (3,0,0) model to best fit our original time series of the annual whooping cough morbidity for the 1921-2018 period. The model predicts better morbidity in a one-step forecast. The incidence rate is expected to be stable at about 1.35 per 100,000 in the next three years, which is close to the 2016 level and lower than those in 2017-2018. Conclusion: The ARIMA (3,0,0) model in our study is an adequate tool for presenting the pertussis morbidity trend and is suitable to forecast near-future disease dynamics, with acceptable error tolerance.

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