Abstract
Sales prediction can help companies to manage resources more effectively, such as cash flow and production, and create better business plans. Sales forecasting allows the company to be proactive instead of reactive. It can help a company in adopting a suitable production policy. It also helps in controlling the inventory and cutting costs on machinery and labor power. In this paper, we examine and compare some machine learning models for predicting future sales, namely Light Gradient Boosting Machine (LGBM), Stacked Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) with LSTM encoded features. The models were evaluated on the dataset containing historical sales data of Russian Software Firm -1C Company. The results manifest that LightGBM performs better than the other two models with 13.24% less Root Mean Square Error (RMSE) compared to stacked LSTM and 26.3% less than MLP with LSTM encoded features.
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References
Bouvrie J (2006) Notes on convolutional neural networks
Chatfield C (1978) The holt-winters forecasting procedure. J Roy Stat Soc Ser C 27(3):264–279
Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (gru) neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE, pp 1597–1600
Dietterich TG et al (2002) Ensemble learning. The handbook of brain theory and neural networks 2(1):110–125
Elalem YK, Maier S, Seifert RW (2022) A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks. Int J Forecast 19:1874–1894
Ensafi Y, Amin SH, Zhang G, Shah B (2022) Time-series forecasting of seasonal items sales using machine learning—a comparative analysis. Int J Inf Manage Data Insights 2(1):100058
Gustriansyah R, Ermatita E, Rini DP (2022) An approach for sales forecasting. Expert Syst Appl 207:118043
Hannan EJ, Kavalieris L (1984) A method for autoregressive-moving average estimation. Biometrika 71(2):273–280
Hans C (2009) Bayesian lasso regression. Biometrika 96(4):835–845
Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12
Hidalgo B, Goodman M (2013) Multivariate or multivariable regression? Am J Public Health 103(1):39–40
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Jain AK, Mao J, Moidin Mohiuddin K (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44
Jha BK, Pande S (2021) Time series forecasting model for supermarket sales using fb-prophet. In: 2021 5th international conference on computing methodologies and communication (IC-CMC). IEEE, pp 547–554
Kaunchi P, Jadhav T, Dandawate Y, Marathe P (2021) Future sales prediction for Indian products using convolutional neural network-long short term memory. In: 2021 2nd global conference for advancement in technology (GCAT). IEEE, pp 1–5
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30
Khandelwal EB (2017) Which algorithm takes the crown: light GBM vs XGBoost?
Kulkarni U, Meena SM, Gurlahosur SV, Mudengudi U (2019) Classification of cultural heritage sites using transfer learning. In: 2019 IEEE fifth international conference on multimedia big data (BigMM). IEEE, pp 391–397
Kulkarni U, Meena SM, Joshua P, Rodrigues K, Gurlahosur SV (2020) Integrated crowdsourcing framework using deep learning for digitalization of Indian heritage infrastructure. In: 2020 IEEE sixth international conference on multimedia big data (BigMM). IEEE, pp 200–208
Kulkarni U, Meena SM, Gurlahosur SV, Bhogar G (2021) Quantization friendly mobilenet (QF-MobileNet) architecture for vision based applications on embedded platforms. Neural Netw 136:28–39
Levitt T et al (1965)Exploit the product life cycle, vol 43. Graduate School of Business Administration, Harvard University
Li D, Lin K, Li X, Liao J, Du R, Chen D, Madden A (2022)Improved sales time series predictions using deep neural networks with spatiotemporal dynamic pattern acquisition mechanism. Inf Process Manage 59(4):102987
Maiorov VN, Crippen GM (1994) Significance of rootmean-square deviation in comparing three-dimensional structures of globular proteins. J Mol Biol 235(2):625–634
Nguyen HD, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. Int J Inf Manage 57:102282
Pan S-Y, Liao Q, Liang Y-T (2022) Multivariable sales prediction for filling stations via GA improved BiLSTM. Petrol Sci 19(5):2483–2496
Peter D, Silvia P (2012) Arima vs. arimax–which approach is better to analyze and forecast macroeconomic time series. In: Proc 30th international conference mathematical methods in economics, vol 2, pp 136–140
Punam K, Pamula R, Jain PK (2018) A two-level statistical model for big mart sales prediction. In: 2018 international conference on computing, power and communication technologies (GUCON). IEEE, pp 617–620
Ranstam J, Cook JA (2018) Lasso regression. J Br Surg 105(10):1348–1348
Sharma AK, Goel N, Rajput J, Bilal M (2020) An intelligent model for predicting the sales of a product. In: 2020 10th international conference on cloud computing, data science & engineering (confluence). IEEE, pp. 341–345
Soni M (2018) Understanding architecture of LSTM cell from scratch with code
Sreekanth P, Kulkarni U, Shetty S, Meena SM (2018) Head pose estimation using transfer learning. In: 2018 international conference on recent trends in advance computing (ICRTAC). IEEE, pp 73–79
Stone CJ (1985) Additive regression and other nonparametric models. Ann Stat 13(2):689–705
Sugiarto VC, Sarno R, Sunaryono D (2016) Sales forecasting using holt-winters in enterprise resource planning at sales and distribution module. In: 2016 international conference on information & communication technology and systems (ICTS). IEEE, pp 8–13
Svetnik V, Liaw A, Tong C, Christopher Culberson J, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947–1958
Verstraete G, Aghezzaf E-H, Desmet B (2020) A leading macroeconomic indicators’ based framework to automatically generate tactical sales forecasts. Comput Ind Eng 139:106169
Yin L, Ma P, Deng Z (2021) Jlgbmloc: a novel high-precision indoor localization method based on lightGBM
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Kulkarni, U. et al. (2024). Future Sales Prediction Using Regression and Deep Learning Techniques. In: Shetty, N.R., Prasad, N.H., Nagaraj, H.C. (eds) Advances in Communication and Applications . ERCICA 2023. Lecture Notes in Electrical Engineering, vol 1105. Springer, Singapore. https://doi.org/10.1007/978-981-99-7633-1_33
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DOI: https://doi.org/10.1007/978-981-99-7633-1_33
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