E-Commerce Price Forecasting Using LSTM Neural Networks

Bakir, Houda; Chniti, Ghassen; Zaher, Hédi

doi:10.18178/ijmlc.2018.8.2.682

Cited by 20 publications

(8 citation statements)

References 21 publications

(18 reference statements)

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“…The model performance was tested and the results show daily solar radiation can be represented using the ARIMA model, while monthly solar radiation can be represented using the SARIMA model with 12 lags. In paper [11], a robust forecasting model using a Long Short-Term Memory (LSTM) neural network and Support Vector Regression (SVR) were constructed to predict phone prices in European markets. For these two methods, they conducted a comparative analysis of time series forecasting models.…”

Section: Related Workmentioning

confidence: 99%

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

2022

KSII TIIS

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Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for smallscale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

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Section: Related Workmentioning

confidence: 99%

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

2022

KSII TIIS

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“…Neural networks may also forecast price using competitors' data from their sites and general data on operation costs. LSTM and SVR neural networks are useful to predict the price on the phone market for the next day (Bakir, Chniti, and Zaher, 2018). The optimization of e-commerce prices using neural network is also beneficial and widely implemented (Peng and Liu, 2007).…”

Section: Previous Studies' Overviewmentioning

confidence: 99%

Data Science and Marketing in E-Commerce Amid COVID-19 Pandemic

Fedirko¹,

Zatonatska²,

Wołowiec³

et al. 2021

ERSJ

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Purpose:The objective of this study involves the determination of data-driven solutions needed to increase the usability of e-commerce systems and its profitability. Design/Methodology/Approach: In the research implementation process, logic generalization and induction to identify and analyze the most beneficial data science tools in e-commerce. deign of the study is to generalize existing approaches of data science usage in e-commerce, to develop practical recommendations to ensure the competitive advantages of e-commerce market participants and to estimate the cost of technical tools needed to launch the data science project in e-commerce. Findings: The results clearly demonstrate that in 2020 businesses that have e-commerce system were financially successful and in next 3 years online sales will increase rapidly. The simple analytics will not cover the demand of online business and it is needed to implement advanced data-driven decisions now. Practical Implications: The present research provides generalized knowledge on how to launch a data science project in e-commerce and how to choose the best programming and visualization app to ensure the profitability of a project. The scientific paper gives an instruction on the marketing contribution analysis, which is the tool of key importance for online marketplaces. Originality/Value: The main research value drawn from the study is to launch the datadriven models in e-commerce company it is needed to observe the real business need and available data, find the best programming and visualization tools. It was defined that the most beneficial data science solutions are demand forecasting, estimation of the marketing contribution, customers clustering, recommendation system and customers' attitude analysis. The main business need for each e-commerce company is to estimate the contribution of all marketing channels and advertisement formats separately. This issue may be easily handled with a regression modelling, which helps to understand a set of factors influencing sales.

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“…Percobaan deep learning LSTM di bidang cuaca dan iklim hingga saat ini sangat minim ditemui. Namun aplikasi lainnya lebih banyak ditemui pada bidang agronomi [15,16,17,18,19], kualitas udara [20,21], perkembangan bahasa [22,23,24,25] bahkan ecommerce yang sedang berkembang pesat saat ini [26,27]. Penelitian yang dilakukan oleh Shi [28] menemukan LSTM dapat menghitung jumlah curah hujan secara spasial dan temporal dengan korelasi 0,908.…”

Section: Pendahuluanunclassified

Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (Lstm)

Supriyadi¹

2021

JMG

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Saat ini metode deep learning dapat diaplikasikan untuk memprediksi suatu kejadian, seperti memprediksi cuaca suatu wilayah. Salah satu contoh deep learning yang cocok digunakan pada jenis data time series adalah LSTM. Penelitian ini menerapkan metode deep learning LSTM dengan jumlah layer 200, perbandingan data training dengan data test sebesar 9:1, serta mengukur nilai RMSE dan RMSE update hasil validasi dan prediksi beberapa hari ke depan. Data yang digunakan terdiri dari pengukuran suhu udara, kelembaban udara, kecepatan angin, dan tekanan udara selama bulan Januari dan Februari 2019. Data bulan Januari digunakan sebagai data training dan test untuk melakukan validasi prakiraan, sedangkan data bulan Februari digunakan sebagai pembanding dari hasil prediksi deep learning LSTM. Hasil penelitian menunjukkan RMSE seluruh validasi parameter cuaca nilainya semakin baik ketika menggunakan LSTM dengan update. Diperoleh RMSE update untuk parameter suhu, kelembaban, kecepatan angin, dan tekanan udara masing-masing bernilai 0,576; 2,8687; 2,1963; dan 1,0647. Sedangkan prediksi suhu udara, kelembaban, kecepatan angin, dan tekanan udara untuk 1 hari ke depan (1 Februari 2019) masing-masing sebesar 1,0337; 6,3413; 2,8934; dan 1,4313. Dari parameter tersebut hanya parameter suhu dan kelembaban udara yang mengalami pertambahan RMSE seiring bertambahnya waktu. Sedangkan parameter kecepatan angin dan tekanan udara mengalami penurunan di hari ketiga dan meningkat secara kontinu hingga satu bulan ke depan.

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E-Commerce Price Forecasting Using LSTM Neural Networks

Cited by 20 publications

References 21 publications

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

Data Science and Marketing in E-Commerce Amid COVID-19 Pandemic

Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (Lstm)

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