Performance of state space and ARIMA models for consumer retail sales forecasting

Ramos, Patrícia; Santos, Nicolau; Rebelo, Rui

doi:10.1016/j.rcim.2014.12.015

Cited by 176 publications

(84 citation statements)

References 30 publications

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“…In comparison of Ramos et al [10], the results did not show any difference between the state space models and ARIMA model with automatic algorithms in forecasting sales of women's footwear products. Fabianová et al [11] made an analysis of refrigerator sales from a retail store.…”

Section: Literature Reviewmentioning

confidence: 60%

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Žunić¹,

Korjenic²,

Hodžić³

et al. 2020

IJCSIT

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This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.

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Section: Literature Reviewmentioning

confidence: 60%

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Žunić¹,

Korjenic²,

Hodžić³

et al. 2020

IJCSIT

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“…Therefore, we propose combining the absolute volumes from ABV and LBV using a Kalman-filter-based state space model (SSM). The state space model or state space time series analysis (as applied in this work) has applications in many different fields [21][22][23][24]. Wallerman et al [25] presented a Bayesian state space model of forest attributes using field measurements and remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation

2015

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To reduce hydrological uncertainties in the regular monitoring of poorly gauged lakes and reservoirs, multi-dimensional remote sensing data have emerged as an excellent alternative. In this paper, we propose three methods to delineate the volume of such equipotential water bodies through a combination of altimetry (1D), Landsat (2D) and bathymetry (2D) data, namely an altimetry-bathymetry-volume method (ABV), a Landsat-bathymetry-volume method (LBV) and an altimetry-Landsat-volume-variation method (ALVV). The first two data products are further merged by a Kalman-filter-based state space model (SSM) to obtain a combined estimate (CSSME) time series and near future prediction. To validate our methods, we tested them on the well-measured Lake Mead and further applied them on the poorly gauged Aral Sea, which has inaccurate bathymetry and very limited ground observation data. We updated the lake bathymetry of the Aral Sea, which was more than half a century old. The resultant remote sensing products have a very good long-term agreement among each other. The Lake Mead volume estimations are very highly coherent with the ground observations for all cases (R 2 > 0.96 and NRMSE < 2.1%), except for the forecast (R 2 = 0.75 and NRMSE = 3.7%). Due to lack of in situ data for the Aral Sea, the estimated volumes are compared, and the entire Aral Sea LBV and ABV have R 2 = 0.91 and NRMSE = 5.5%, and the forecast compared to CSSME has R 2 = 0.60 and NRMSE = 2.4%.

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“…It builds and makes predictions through historical time series data. Ramos et al [19] used the ARIMA model to forecast consumer retail sales. In this paper, the ARIMA ( , , ) model is used to build a model for spare parts prediction based on the historical requirement of spare parts ( ).…”

Section: Analysis Of Alternative Prediction Modelsmentioning

confidence: 99%

Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation

Wang

Pan

Wang

et al. 2018

Mathematical Problems in Engineering

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Probability of spare parts sufficiency is crucial in the process of the normal operation of businesses, especially for the airline company. However, higher support sufficiency could inevitably lead to the increase of inventory cost of spare parts and restrict a company's efficiency. Therefore, it is important for businesses to reduce material cost on the premise of normal operation in order to accurately predict spare parts requirements based on reasonable models. The purpose of this paper is to solve problems with the evaluation of spare parts prediction models and to improve efficiency of company. Firstly, this paper summarizes a series of prediction models of spare parts requirements and applies the grey comprehensive correlation degree to rank the models. Secondly, the method of association rules mining is used to discover the association relationships between the types of spare parts and the prediction models. Finally, a case study in aviation is given to demonstrate the feasibility of the methodology, and optimal prediction models are recommended for aircraft spare parts. In accordance with the association relationships, the applicable prediction model can be provided in terms of different types of spare parts. This model will greatly enhance the work efficiency of spare parts prediction and improve the prediction tasks for the aircraft companies.

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Performance of state space and ARIMA models for consumer retail sales forecasting

Cited by 176 publications

References 30 publications

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation

Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation

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