# Pre-launch Fashion Product Demand Forecasting Using Machine Learning Algorithms

Marios Arampatzis, G Eorgios Theodoridis, Athanasios Tsadiras

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Marios Arampatzis, G Eorgios Theodoridis, Athanasios Tsadiras. Pre-launch Fashion Product Demand Forecasting Using Machine Learning Algorithms. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.362-372, 10.1007/978-3-031-34107-6_29. hal-05498058

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# Pre-launch Fashion Product Demand Forecasting Using Machine Learning Algorithms

Marios Arampatzis(✉), G eorgios Theodoridis, and Athanasios Tsadiras

Aristotle University of Thessaloniki, Thessaloniki, Greece
{agmarios,tsadiras}@econ.auth.gr, ttgeorgios@csd.auth.gr

**Abstract.** The importance of sales forecasting is undeniable. Predicting the sales of the businesses' products have impact in more than one department of a company. In most cases successful forecasting is a complicated issue especially when the product has not or has just been released to the market hence there are no historical data of sales of that exact product. The current research focuses on addressing a problem that bibliographically is not widely researched, that is forecasting the sales of new fashion products before their market release via analyzing their fundamental features and the historical sales data of other, previously released products. To generate accurate results and present a complete strategy various Machine Learning algorithms are modeled, trained, and compared to solve the above mentioned problem. The algorithms examined are categorized as non-linear, ensemble and neural networks methods, and the hyperparameters of non-linear and ensemble algorithms are optimized via Grid Search and the hyperparameters of neural networks are optimized via Bayesian optimization. The results reveal that the Convolutional Neural Network (CNN) method is outperforming all the examined algorithms according to Weighted Absolute Percentage Error (WAPE) and Mean Absolute Error (MAE) metrics. No specific category of methods among non-linear, ensemble and neural networks, was found to perform better.

**Keywords:** Machine Learning · Sales Forecasting · New Product · Pre-Launch · Non-Linear Methods · Ensemble Methods · Neural Networks

## 1 Introduction

It has been noticed that sales forecasting undoubtedly plays a vital role in decision making, particularly in relation to manufacturing scheduling, and inventory replenishment. Inaccuracies in forecasting could cause excess inventories or lost sales [12].

Both the designers and retailers of clothing products need to face the inherent problem of short product life cycles therefore it is necessary for businesses to timely forecast the demand of these goods [21]. An accurate demand forecasting is vital for clothing industry because of problems such as a) inadequate inventory level, b) inferior customer service, and c) substantial number of lost sales [9].

One of the factors that makes the forecasting of clothes' demand difficult is the lack of historical sales (or demand) data for every new collection of fashion items that the
designers and retailers introduce. Since there is no historical data for these new products, the researchers or managers must rely on sales of previous collections taking into account the characteristics of previous similar fashion clothes. Additionally, the high degree of uncertainty involved when introducing new products increase even more the difficulty of new product sales forecasting [3].

However, the importance of accurate forecasting is unquestionable. By predicting the demand of their products, companies will assist their business planning, budgeting, and risk management. Moreover, it offers them the chance to optimally allocate their resources and estimate their costs and revenue.

The importance of the connection between forecasts and resource decisions cannot be understated by manufacturing organizations. Additionally, without sales forecasting there could be no long-term production planning of future sales that allows sufficient capacity and labor needs [22].

Considering the facts that have been mentioned above the current work attempts to successfully forecast the sales of newly released fashion products. The dataset that is being used in this study contains 5,577 new clothing products and their sales in the first 12 weeks after their initial release to the market. Ensemble Machine Learning algorithms and Neural Networks are extensively trained, optimized, and applied to accurately predict sales. The use of Ensemble Machine learning algorithms and Neural Networks for that kind of problem is the innovation of the present research. Additionally, have been applied Non-Linear algorithms with the same aim.

The structure of the work is as follows. In Sect. 2, a literature review is presented where related work is being discussed. The description of the dataset with details regarding the structure of our experiments (the inputs, the algorithms that are being used and the outputs) are given in Sect. 3. Section 4 presents the main characteristics of the algorithms that are applied, while Sect. 5 presents the utilized-evaluation metric. Finally, in Sect. 6 the results of the algorithms are being presented, commented, and discussed, with Sect. 7 to conclude the study with the synopsis, the findings and proposals for future research.

## 2 Literature Review

Introducing new products can provide a competitive advantage as well as a long-term financial return on investment. On the other hand, new product development is also a lengthy process with a high risk of failure but absolutely necessary because in this way companies are able to expand their market share with new buyers [19]. The development of a new product allows each company to become competitive, but many of them consider new product sales forecasting as one of the most difficult forecasting problems they encounter [3]. A small number of publications have dealt with this problem, and they are discussed below.

One of the main retailers' challenges is pricing and predicting demand for products that have never been sold before, especially in the case of retailers where these products correspond for the majority of sales and revenue. To tackle this challenge, in [8], a plethora of machine learning techniques has been utilized to estimate historical lost sales and predict future demand of new products.
Other researchers have focused on forecasting the sales of new products using time series. More specifically, in [7], multiple time-series have been analyzed and grouped together to create a model that is able to predict the sales of a new product. The goal was to forecast the sales of a new product using the sales history of other products. In order to achieve this task, the k-nearest Neighbor (KNN) algorithm and different Encoder-Decoder Sequence models have been used.

In another project, presented in [16], the researchers used the Visuelle dataset which provides the sales of the first 12 week of clothing products, the images of these clothes, textual data like category, color, fabric and release date of the goods and google-trends relating to these cloths. Their task was, to predict the first 6 weeks sales of the clothes, by using various combinations of inputs. The algorithms they used are kNN, GBoosting, MultiModal-RNN and a custom novel “GTM-transformer”. One conclusion that could be drawn was that the more features of the products have been considered the better results the algorithms give us.

Similarly, in [17], the researchers had at their disposal the Visuelle 2.0 which includes the sales of the twelve first weeks of 5,355 different new released fashion products. In their first attempt to predict the sales of these new products, they assumed that they knew the first two weeks of these products’ sales and they had to forecast the sales of the other ten weeks. In their second attempt they assumed that they know they know the sales of two continuous weeks and each time they predict the sales of the following week. The algorithms that they used are the k-Nearest Neighbors and an Attention-based RNN.

Finally with the same Visuelle there was an attempt to create and introduce time series data that the authors call Potential Performance [10]. These time series show the “potential” sales of new products for many weeks before their release. Afterwards, by inputting these time series to their models, they forecast the sales of the first weeks of the aforementioned products by using Gradient Boosting, RNN and the “GTM-transformer” algorithms.

# 3 Dataset

The problem in our study, as has been mentioned above has as aim the forecast of the sales of new released products, particularly of new clothes. The VISUELLE dataset is used [16] which includes two pre-defined datasets with the training and testing sets. The dataset consists of a total of 5,577 new clothing products. Additionally, generic categorical information is given for each one of these clothes, like their color (grey, red, etc.), fabric (georgette, fries, etc.), category (short sleeves, long sleeve, etc.), season of their collection (Spring-Summer 2019, Autumn-Winter 2019, etc.) and finally one more supplementary category, named “extra” that declares one more attribute for each product of our dataset. All these nominal variables have been converted into numerical form with Categorical Boosting (CatBoost) Encoder [14]. CatBoost implements several ways to deal with categorical features. Uses a more efficient strategy which reduces overfitting and allows to use the whole dataset for training [6].

Furthermore, aforementioned “Potential Performance (POP)” [10] is also available and utilized within the current study. The POP is the potential sales, of 52 weeks, that the products would have if they have been released 52 weeks before the date that they have been, actually, released to the market.
All these features are used as model inputs to train and optimize the algorithms. The outputs of our dataset are the forecasted sales of the first 6 weeks after the date that the clothes have been released to the market by the designers. The training set consists of 5,080 products and the testing set consists of 497. In conclusion, with the help of the POP timeseries and the other features of the dataset, the sales of the 6 first weeks of the products must be forecasted. The inputs and outputs are being described in the Fig. 1.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Inputs, Processing and Outputs.</figcaption></figure>

# 4 Categories of Predictive Methods

The solution of our problem requires, among others, non-linear regression models. Furthermore, Ensemble Methods and Neural Networks will be used to potentially increase the prediction accuracy of products' sales. Taking the above into account, the algorithms presented in paragraphs 4.1, 4.2 and 4.3 are considered as viable options. Additionally, the results of non-linear and ensemble algorithms via grid search and 10-fold Cross Validation (CV) have been optimized and the results of Neural Networks algorithms have been optimized via Bayesian optimization, so the Mean Absolute Error is minimized. The final hyperparameters searched and selected (bold) are also presented.

## 4.1 Non-linear Methods

Support Vector Machines (SVMs) are a set of supervised learning methods used for classification, regression (Support Vector Regression, SVR), and outlier detection [5]. The SVM algorithm was developed in 1995 and is based on statistical learning theory [4]. In the case of regression, the goal is to construct a hyperplane that lies “close” to as many of the data points as possible. Therefore, the objective is to choose a hyperplane
with small norm while simultaneously minimizing the sum of the distances from the data points to the hyperplane.

The parameters and searched values that have been used for our experiments are the following:

* Regularization parameter C: **0.1, 1, 10, 100, 200, 500, 600, 1000**

* Kernel coefficient gamma: 0.001, 0.01, 0.1, **1**

* Kernel type: **'rbf', 'poly', 'sigmoid'**

Decision Tree is a tree data structure that consists of an arbitrary number of nodes and branches at each node. The values of the input variable(s) consider a particular function in the training stage [13].

The parameters and searched values that have been used for our experiments are the following:

* The maximum depth of the tree: 1, 2, **3**, 4, 5, 10, 15, 20, 50, 70, 80, 90, 100, 110, 200, 300

* The minimum number of samples required to split an internal node: 35, 45, 55, 100, 300, 500, **1000**, 1500, 2500, 4000.

## 4.2 Ensemble Methods

Random Forest (RF) is an algorithm developed in 2001 and it is appropriate for both classification and regression problems. It functions by constructing multiple decision trees while training and outputting a value or a class in case of regression or classification, respectively. Additionally, Random Forest can handle up to thousands of explanatory variables and it is suitable for demonstrating the nonlinear effect of input variables, and it can model complex, non-linear interactions among variables [1].

The parameters and searched values that have been used for our experiments are the following:

* The maximum depth of the tree: 1, 2, **3**, 4, 8, 10, 12, None

* The number of trees in the forest: 10, 25, **50**, 100, 200, 300, 600, 800, 1000, 1200, 1400, 1600

* The maximum number of features used: square root of features, log2 of features, None

The Gradient Boosting method strategically resamples the training data to provide the most useful information for each consecutive model and is more accurate in predictions because it develops multiple models in sequence by putting emphasis on the training cases that are difficult to estimate [23]. This is the reason it is included in the ensemble methods.

The parameters and searched values, that have been used for our experiments are the following:

* Maximum depth of the individual regression estimators: **1, 2, 3, 4, 8, 10, 12, None**

* The number of boosting stages to perform: 10, 20, 30, 40, 50, 70, **100**, 200, 300, 600, 800, 1000
* The maximum number of features used: square root of features, log2 of features, None

XGBoost has been described as a scalable end-to-end tree boosting system. This method, is widely used by data scientists and analysts to achieve results in many machine learning challenges. It has been proved that this method gives state-of-art results on a wide range of problems. The most important factor behind the success of XGBoost is that it can cope with multiple different scenarios hence being robust [2].

The parameters and searched values that have been used for our experiments are the following:

* Maximum depth of the tree: 1, 2, 3, 4, 8, 10, 12, None

* Maximum number of leaves: 1, 5, 10, 15, 20, 25, 30, 50, 100, 150

* The number of boosting stages to perform: 10, 25, **50**, 75, 100, 200, 300, 600, 800, 1000

* L1 regularization term weights: 0.1, **0.5**, 0.8

* L2 regularization term of weights: 0, 1, **1.5**, 2

* Booster used: **GBTree**, DART

LightGBM is a gradient boosting decision tree (GBDT) [11]. This method contains two novel techniques: Gradient-based One-Side Sampling and Exclusive Feature Bundling to deal with large number of data instances and large number of features respectively. Previous experiments on multiple datasets show that, LightGBM is twenty times faster during training procedure than the conventional GBDT and at the same time achieves similar accuracy in the results.

The parameters and searched values that have been used for our experiments are the following:

* The maximum depth of the tree: 1, 2, **3**, 4, 8, 10, 12, None

* Maximum number of leaves: 5, 10, 15, **20**, 50, 100, 150

* The number of boosting stages to perform: 1, 5, 10, **20**, 50, 70, 100, 200, 300, 600, 800, 1000

* L1 regularization term on weights: 0.001, 0.01, 0.1, **0.5**, 0.6, 0.7, 0.8

* L2 regularization term on weights: 0, 1, 1.5, 2, 2.5, **3**, 3.5, 4

* Minimum number of data needed in a child (leaf): 1, 5, **10**, 20, 40, 80

* Booster used: **GBTree**, **DART**.

## 4.3 Neural Networks

Neural Networks are algorithms that are patterned after the structure of the human brain. They contain several mathematical equations that are used to simulate humans' brain processes such as learning and memory. Many different types of neural network training algorithms have been developed. Two of them are the Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) and the Convolutional Neural Network (CNN) [20].

RNNs may alleviate potential problems by using recursive structures between the previous frame and the current frame to capture the long-term contextual information to make a better prediction and, Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) introduces the concepts of memory cell and a series of gates to
dynamically control information flow, which well solves the vanishing gradient problem [18].

The architecture of the network is the following:

* Number layers: 5

* Number of epochs: 100

* Batch size: 32

To optimize the parameters of the LSTM-RNN, a Bayesian Optimization is used, instead of grid search.

* The units, the number of neurons connected to the layer holding the concatenated vector of hidden state and input. Values: 16–128, step 16. **Optimal**: layer 1 set to 128 units, 2 to 112, 3 to 80, 4 to 16 and 5 to 16.

* LSTM Activation function. Values: “relu”, “tanh”, “sigmoid”. **Optimal**: for 1 set to “tanh”, for 2 to “sigmoid”, for 3 to “tanh”, for 4 to “tanh” and for 5 to “tanh”.

* The dropout rate of every dropout layer. Values: from 0 to 0.5, step 0.1. **Optimal**: layer 1 set to 0.4 units, 2 to 0.2, 3 to 0.2, 4 to 0 and 5 to 0.

* The recurrent dropout. Values: from 0 to 0.5, step 0.1. **Optimal**: layer 1 set to 0.1, 2 to 0.1, 3 to 0.0, 4 to 0.0 and 5 to 0.0

Convolutional Neural Network (CNN), auto-encoders, and deep belief network are widely known models in deep learning. The CNN are deep learning model mainly applied in image classification problems. Recently, different CNN architectures are applied on multi-channel time series data for activity recognition problems [15].

The architecture of the network is the following:

* Convolutional layer: Conv1D

* Number of epochs: 100

* Batch size: 32

To optimize the parameters of the CNN, a Bayesian Optimization is used.

* Dropout rate. Values: from 0.1 to 0.5. **Optimal**: 0.156954

* Learning rate. Values: from 0.001 to 0.01. **Optimal**: 0.00457.

# 5 Description of the Metrics

A set of evaluation metrics, including Weighted Absolute Percentage Error (WAPE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE), and Root Mean Square Error (RMSE) are used to compare the forecasted values with the real actual values. Their formulas are presented in Table 1.

WAPE is more effective when it is important to prioritize popular items and reduce the error effect of non-popular items over the evaluation period. It is a more accurate evaluation metric as it would calculate the percentage error over the total item sales across 6 weeks.
<table><caption>**Table 1.** The metrics' formulas and descriptions (n = the total number of observations)</caption><thead><tr><th>Metrics' Formulas</th><th>Description</th></tr></thead><tbody><tr><td>WAPE = <sup>Σ<sup>n</sup><sub>t=1</sub> |Actual Value<sub>t</sub> - Forecasted Value<sub>t</sub>|</sup>&frasl;<sub>Σ<sup>n</sup><sub>t=1</sub> |Actual Value<sub>t</sub>|</sub></td><td>It is the sum of the absolute error between the actual and forecasted value normalized by the total demand or sales of all products or items</td></tr><tr><td>MAE = <sup>Σ<sup>n</sup><sub>t=1</sub> |Actual Value<sub>t</sub> - Forecasted Value<sub>t</sub>|</sup>&frasl;<sub>n</sub></td><td>Represent the average magnitude of the errors in a set of predicts, without considering their direction</td></tr><tr><td>sMAPE = <sup>1/n Σ<sup>n</sup><sub>t=1</sub> <sup>|Forecasted Value<sub>t</sub> - Actual Value<sub>t</sub>|</sup>&frasl;<sub>|Actual Value<sub>t</sub>| + |Forecasted Value<sub>t</sub>|</sub></sup>&frasl;<sub>2</sub></td><td>Is an accuracy measure based on relative errors</td></tr><tr><td>RMSE = <sup>√Σ<sup>n</sup><sub>t=1</sub> (Actual Value<sub>t</sub> - Forecasted Value<sub>t</sub>)<sup>2</sup></sup>&frasl;<sub>n</sub></td><td>Is a quadratic scoring metric which represent the average magnitude of the error</td></tr></tbody></table>

# 6 Results

After the optimization of the hyperparameters via Grid Search and Bayesian optimization the models could now be tested using the test data.

Table 2 presents the results of the metrics that the algorithms calculated after rounding of the model outputs, as sales of fashion product can only have integer values. The best value for each metric is in bold.

<table><caption>**Table 2.** The results of the metrics by considering the exact results of the algorithms.</caption><thead><tr><th>Algorithms</th><th>WAPE</th><th>MAE</th><th>sMAPE</th><th>RMSE</th></tr></thead><tbody><tr><td colspan="5">Non-Linear methods</td></tr><tr><td>SVR (8)</td><td>2.229</td><td>55.872</td><td>0.843</td><td>64.384</td></tr><tr><td>Decision Tree (2)</td><td>0.898</td><td>32.107</td><td>0.634</td><td>37.884</td></tr><tr><td colspan="5">Ensemble Methods</td></tr><tr><td>GBR (7)</td><td>0.948</td><td>31.974</td><td>0.628</td><td>37.349</td></tr><tr><td>Random Forest (3)</td><td>0.913</td><td>31.823</td><td>0.628</td><td>37.279</td></tr><tr><td>XGBoost (5)</td><td>0.942</td><td>32.016</td><td>0.628</td><td>37.422</td></tr><tr><td>LightGBM (4)</td><td>0.934</td><td>31.547</td><td><strong>0.620</strong></td><td><strong>36.903</strong></td></tr><tr><td colspan="5">Neural Networks</td></tr><tr><td>LSTM-RNN (6)</td><td>0.947</td><td>32.056</td><td>0.634</td><td>37.284</td></tr><tr><td>CNN (1)</td><td><strong>0.783</strong></td><td><strong>31.224</strong></td><td>0.649</td><td>38.851</td></tr></tbody></table>
In this study, WAPE is considered the primary evaluation metric, as it common in studies regarding forecasting sales of new fashion products [10, 16]. The rank of each algorithm based on the WAPE metric is being denoted in Table 2, with the number into the parenthesis next to the name of the method.

The results in Table 2 and Fig. 2, denote that the best algorithm, according the WAPE and MAE, is the CNN followed by the Decision Tree method according to WAPE and by LightGBM according to MAE. The best algorithm according to the sMAPE and RMSE metrics is LightGBM. The other examined algorithms perform relatively similarly well, with the exemption of SVR that performs poor.

A conclusion that can be drawn is that there is not a specific category of algorithms that seems to perform better in our study, since the best algorithm according to WAPE (CNN) belongs to Neural Networks, the second best (Decision Tree) belongs to non-linear methods, while two Ensemble methods (Random Forest and LightGBM) have the third and fourth position. This means that in forecasting sales of new fashion products, all kinds of algorithms should be examined, regardless of the categories that they belong, since the complex nature of the specific problem can make any of them a potential good solution.

<figure><img src="image_5.png" /><figcaption>Fig. 2. The algorithms' scores according to the WAPE metric.</figcaption></figure>

# 7 Conclusions and Suggestions for Future Research

This research has as aim to forecast the sales of new fashion products before the company release them to the market. For that reason, eight different Machine Learning algorithms separated in the three categories (non-linear, ensemble and Neural Networks) have been used. The results show that the best machine learning method based on the metrics WAPE and MAE is the CNN algorithm followed by the Decision Tree method according
to WAPE. No specific category of methods among non-linear, ensemble and neural networks, was found to perform better in our study. This leads us to the conclusion that regardless the category that an algorithm belongs, it should be carefully examined, because the complexity of the particular problem may make any of them a candidate for a good solution.

In the future, we plan to verify the results that were drawn from this study, on other similar datasets. Furthermore, it would be interesting to study new ways to create timeseries of hypothetical demand of new fashion products, that exist even before the release of the products (similar to the POP timeseries that were used in this paper) and use them for forecasting the actual demand. Moreover, a study regarding the estimation of the importance of each fashion product characteristic towards its sales, could be of great importance for fashion companies.

## References

1. Breiman, L., Cutler, A.: Random forest, machine learning. Stat. Dept. Univ. Calif. 1, 33 (2001)

2. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

3. Ching-Chin, C., Ieng, A.I.K., Ling-Ling, W., Ling-Chieh, K.: Designing a decision-support system for new product sales forecasting. Expert Syst. Appl. 37(2), 1654–1665 (2010)

4. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

5. Coussement, K., Van den Poel, D.: Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst. Appl. 34(1), 313–327 (2008)

6. Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support (2018). arXiv preprint arXiv:1810.11363

7. Ekambaram, V., Manglik, K., Mukherjee, S., Sajja, S.S.K., Dwivedi, S., Raykar, V.: Attention based multi-modal new product sales time-series forecasting. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3110–3118 (2020)

8. Ferreira, K.J., Lee, B.H.A., Simchi-Levi, D.: Analytics for an online retailer: demand forecasting and price optimization. Manuf. Serv. Oper. Manag. 18(1), 69–88 (2016)

9. Huang, H., Liu, Q.: Intelligent retail forecasting system for new clothing products considering stock-out. Fibres Text. Eastern Eur. 1(121), 10–16 (2017)

10. Joppi, C., Skenderi, G., Cristani, M.: POP: mining potential performance of new fashion products via webly cross-modal query expansion. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision–ECCV 2022, ECVV 2022. LNCS: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVIII, vol. 13698, pp. 34–50. Springer: Cham (2022). https://doi.org/10.1007/978-3-031-19839-7_3

11. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

12. Lawrence, M., O’Connor, M., Edmundson, B.: A field study of sales forecasting accuracy and processes. Eur. J. Oper. Res. 122(1), 151–160 (2000)

13. Loh, W.Y.: Classification and regression trees. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(1), 14–23 (2011)

14. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
15. Sateesh Babu, G., Zhao, P., Li, X.-L.: Deep convolutional neural network based regression approach for estimation of remaining useful life. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 214–228. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_14

16. Skenderi, G., Joppi, C., Denitto, M., Cristani, M.: Well googled is half done: multimodal forecasting of new fashion product sales with image-based google trends (2021). arXiv preprint arXiv:2109.09824

17. Skenderi, G., Joppi, C., Denitto, M., Scarpa, B., Cristani, M.: The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2241–2246 (2022)

18. Sun, L., Du, J., Dai, L.R., Lee, C.H.: Multiple-target deep learning for LSTM-RNN based speech enhancement. In: 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA), pp. 136–140. IEEE (2017)

19. Thomas, R.J.: New product development: managing and forecasting for strategic success. University of Texas Press (1993)

20. Tu, J.V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49(11), 1225–1231 (1996)

21. Van Steenbergen, R.M., Mes, M.R.: Forecasting demand profiles of new products. Decis. Support Syst. 139, 113401 (2020)

22. Wacker, J.G., Lummus, R.R.: Sales forecasting for strategic resource planning. Int. J. Oper. Prod. Manag. 22, 1014–1031 (2002)

23. Zhang, Y., Haghani, A.: A gradient boosting method to improve travel time prediction. Transp. Res. 58, 308–324 (2015)