Forecast of individual customer’s demand from a large and noisy dataset

Murray, Paul W.; Agard, Bruno; Barajas, Marco

doi:10.1016/j.cie.2018.02.007

Cited by 46 publications

(17 citation statements)

References 42 publications

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“…Studies may expand this research to other product categories and their sensitivity to external factors similar to the ones used in this study. Forecasting with hierarchical or Retail sales of alcoholic beverages grouped time-series models in a big data environment is an option that allows state-space and space-time-related analysis (Hyndman and Athanasopoulos, 2018;Murray et al, 2018). Combining SARIMAX time-series analysis with machine-learning techniques, such as neural networks, can also be considered (Aburto and Weber, 2007).…”

Section: Implications Limitations and Future Researchmentioning

confidence: 99%

Comparing the day temperature and holiday effects on retail sales of alcoholic beverages – a time-series analysis

Hirche

Haensch

Lockshin

2021

IJWBR

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Purpose Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts. Design/methodology/approach Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects. Findings The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories. Originality/value This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.

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Section: Implications Limitations and Future Researchmentioning

confidence: 99%

Comparing the day temperature and holiday effects on retail sales of alcoholic beverages – a time-series analysis

Hirche

Haensch

Lockshin

2021

IJWBR

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“…Therefore, it is necessary to use techniques capable of reflecting such behavior and this is where, lately, the use of artificial intelligence techniques is being applied in forecasting demand with much greater force. By the year 2018, in [10] propose a method that uses forecasting and data mining tools, applying them with a higher level of precision at the client level than other traditional methods. Subsequently, [11] examines the exponential smoothing model in the context of supply chain use and logistics forecasting, performing microeconomic time series forecasting.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

Lorente-Leyva

Alemany

Peluffo-Ordóñez

et al. 2020

Machine Learning, Optimization, and Data Science

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This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.

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“…Semakin kecil nilai RMSE menunjukkan semakin baik nilai yang direkomendasikan oleh sistem. RMSE dihitung dengan persamaan [7] :…”

Section: • Pengujianunclassified

Pengaruh Penggunaan Synonym Recognition dan Spelling Correction pada Hasil Aplikasi Penilaian Esai dengan Metode Longest Common Subsequence dan Cosine Similarity

2019

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Essay assessment application is a must assess the similarity of meaning of the answer typed by the examinee with the answer key used as a benchmark for the correctness of the answer.Where an essay answer is a natural human language data that can have word synonyms and there is a possibility of input errors caused by typing errors (spelling errors). For this reason, it is necessary to have a study that can measure how influential the use of synonym recognition and spelling correction on the results of essay assessment applications. In this study, the data used to carry out testing were exam data on Indonesian subjects, cultural arts and science with the number of questions for each exam were 5 questions, each of which was attended by 24 students. So that from each test there will be 120 answers. The test results show that the use of synonym recognition and spelling correction on the results of essay assessment applications can improve accuracy and reduce the value of root mean square error (RMSE).aplikasi penilaian esai adalah harus menilai kemiripan makna dari jawaban yan diketik oleh peserta ujian dengan kunci jawaban yang digunakan sebagai patokan kebenaran jawaban. Dimana jawaban esai adalah data bahasa alami manusia yang bisa memiliki sinonim kata dan ada kemungkinan kesalahan input yang disebabkan karena kesalahan pengetikan (kesalahan ejaan). Untuk itu perlu ada sebuah penelitian yang dapat mengukur seberapa berpengaruhnya penggunan synonim recognition dan spelling correction pada hasil aplikasi penilaian esai. Pada penelitian ini data yang digunakan untuk melakukan pengujian adalah data ujian pada mata pelajaran bahasa indonesia, seni budaya dan IPA dengan jumlah soal masing-masing ujian adalah 5 soal yang masing-masing ujian tersebut diikuti oleh 24 pelajar. Sehingga dari setiap ujian akan terdapat sebanyak 120 jawaban. Hasil pengujian menunjukkan bahwa penggunaan synonym recognition dan spelling correction pada hasil aplikasi penilaian esai dapat meningkatkan akurasi dan memperkecil nilai root mean square error (rmse). PENDAHULUANDalam berbagai bahasa pasti memiliki kata-kata yang memiliki kesamaan arti meski kata tersebut berbeda, misalnya dalam bahasa indonesia yaitu kata saya. Dalam bahasa indonesia kata saya memiliki banyak sinonim kata seperti aku, abdi, ana, gua. Jika ada sebuah sistem atau aplikasi yang dibuat untuk memahami bahasa manusia maka sistem atau aplikasi tersebut juga harus dapat memahami kasus seperti kata bersinomin ini. Maka dari itulah dikembangkan suatu cara untuk mengenali sinomin yang disebut synonim recognition. Synonim recognition ini bekerja dengan cara mengubah kata bersinonim kekata utamanya. Selain masalah sinomin, sistem atau aplikasi yang dibuat untuk memahami bahasa manusia juga harus dapat memahami bahkan harus dapat mengoreksi kata yang diketik oleh penguna. Proses seperti ini disebut spelling Correction. Kesalahan dalam mengetik sangat lumrah terjadi dilakukan oleh seseorang. Hal tersebut bisa disebabkan ketidaktahuan atau terburu-buru dalam mengetik. Kesalahan penget...

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Forecast of individual customer’s demand from a large and noisy dataset

Cited by 46 publications

References 42 publications

Comparing the day temperature and holiday effects on retail sales of alcoholic beverages – a time-series analysis

Comparing the day temperature and holiday effects on retail sales of alcoholic beverages – a time-series analysis

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

Pengaruh Penggunaan Synonym Recognition dan Spelling Correction pada Hasil Aplikasi Penilaian Esai dengan Metode Longest Common Subsequence dan Cosine Similarity

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