Forecasting of sales by using fusion of machine learning techniques

Gumani, Mohit; Korke, Yogesh; Shah, Prachi; Udmale, Sandeep S.; Sambhe, Vijay K.; Bhirud, S. G.

doi:10.1109/icdmai.2017.8073492

Cited by 39 publications

(23 citation statements)

References 18 publications

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“…[30] exposes a sales prediction model for retail stores; and Refs. [31,32] propose machine learning models to predict specific companies' sales. In our case, the presented approach could help producers and vendors in several aspects.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018

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Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created.

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Section: Discussion and Future Workmentioning

confidence: 99%

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018

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“…Because favorable prediction results have been obtained by using this model, it is widely used in machine learning competitions. Meanwhile, the XGBoost model performs excellently on the prediction of the sales volume, stock price, and traffic flow (Gurnani et al 2017;Wang and Guo 2020;Lu et al 2020). However, there are few applications of this algorithm for prediction tasks in engineering practices due to its the late advent.…”

Section: Xgboostmentioning

confidence: 99%

Application of time series prediction techniques for coastal bridge engineering

Wei

Yan

et al. 2021

ABEN

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In this study, three machine learning techniques, the XGBoost (Extreme Gradient Boosting), LSTM (Long Short-Term Memory Networks), and ARIMA (Autoregressive Integrated Moving Average Model), are utilized to deal with the time series prediction tasks for coastal bridge engineering. The performance of these techniques is comparatively demonstrated in three typical cases, the wave-load-on-deck under regular waves, structural displacement under combined wind and wave loads, and wave height variation along with typhoon/hurricane approaching. To enhance the prediction accuracy, a typical data preprocessing method is adopted and an improved prediction framework for the LSTM model after the rolling forecast prediction is proposed. The obtained results show that: (a) When making a prediction on data featured with periodic regularity, both the XGBoost and ARIMA models perform well, and the XGBoost model can make predictions multi-step ahead, (b) The ARIMA model can predict just one step ahead based on aperiodic dataset with limited amplitude more accurately, while the XGBoost and LSTM models can predict multi-step ahead with appropriate data preprocessing, and (c) All the three models can predict the data tendency with model updating over time, but the prediction accuracy of the LSTM model is more favorable. The successful application of these three machine learning techniques can provide guidance to resolve engineering problems with time-history prediction requirements.

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“…Let's consider each approach. Among statistical methods, the traditional Auto-Regressive Integrated Moving Average (ARIMA) model has been used as the baseline in most studies for sales forecasting (Müller-Navarra et al, 2015;Pavlyshenko, 2016;Gurnani et al, 2017). However, the traditional ARIMA models cannot handle multivariate features (Bandara , 2019) and also shows poor performance in handling seasonality and trend (Gurnani et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

“…Xiangsheng Xie ( 2008) and Wu et al ( 2012) have adopted two variants of ARIMA; Seasonal ARIMA and Vector Auto-Regressive Moving Average (ARMAV) with the linear trend to handle above properties in sales forecasting tasks. Gurnani et al (2017) show that ARIMA with external regressors is most suitable to model the linearity in time series data, yet fail to capture non-linear patterns (Zhang, 2003).…”

Section: Related Workmentioning

confidence: 99%

Sales forecasting using multivariate long short term memory network models

Helmini

Jihan

Jayasinghe

et al. 2019

Preprint

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In the retail domain, estimating the sales before actual sales become known plays a key role in maintaining a successful business. This is due to the fact that most crucial decisions are bound to be based on these forecasts. Statistical sales forecasting models like ARIMA (Auto-Regressive Integrated Moving Average), can be identified as one of the most traditional and commonly used forecasting methodologies. Even though these models are capable of producing satisfactory forecasts for linear time series data they are not suitable for analyzing non-linear data. Therefore, machine learning models (such as Random Forest Regression, XGBoost) have been employed frequently as they were able to achieve better results using non-linear data. The recent research shows that deep learning models (e.g. recurrent neural networks) can provide higher accuracy in predictions compared to machine learning models due to their ability to persist information and identify temporal relationships. In this paper, we adopt a special variant of Long Short Term Memory (LSTM) network called LSTM model with peephole connections for sales prediction. We first build our model using historical features for sales forecasting. We compare the results of this initial LSTM model with multiple machine learning models, namely, the Extreme Gradient Boosting model (XGB) and Random Forest Regressor model(RFR). We further improve the prediction accuracy of the initial model by incorporating features that describe the future that is known to us in the current moment, an approach that has not been explored in previous state-of-the-art LSTM based forecasting models. The initial LSTM model we develop outperforms the machine learning models achieving 12% - 14% improvement whereas the improved LSTM model achieves 11\% - 13\% improvement compared to the improved machine learning models. Furthermore, we also show that our improved LSTM model can obtain a 20% - 21% improvement compared to the initial LSTM model, achieving significant improvement.

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Forecasting of sales by using fusion of machine learning techniques

Cited by 39 publications

References 18 publications

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Application of time series prediction techniques for coastal bridge engineering

Sales forecasting using multivariate long short term memory network models

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