Time-aware sport goods sale prediction for healthcare with privacy-preservation

Abstract

Sports industry has been playing an important role in achieving good healthcare for public. However, with the advent of COVID-19, sports industry has been influenced significantly and the industry scale is decreased considerably. In this situation, how to accurately predict the sports industry scale in terms of production and consumption is becoming a practical and valuable task, because the whole world’s economy is not growing stably and users’ demand to sport goods is fluctuating sharply. However, three challenges are often existing in the sports industry scale prediction. First of all, there are so many kinds of sport goods that it is hard to quickly predict their future production or consumption scales accurately. Second, for a certain sport commodity, its production or consumption scale is often related to time especially in the COVID-19 environment. Third, sports industry scale data often contain some privacy, which probably disables data stakeholders to disclose their data. In view of these three challenges, a novel sports industry scale prediction approach (named SISP) is proposed for healthcare, which is basically according to time series analysis. Through SISP approach, we can quickly and accurately predict the future production or consumption scales of sport goods, in a privacy-aware way. At last, we validate the feasibility of the proposed SISP approach in this paper.

1. Introduction

With the gradual progress of economy all over the world, people are more and more interested in living in a healthy manner [1], [2], [3]. Inspired by this health goal, people pay more attentions to various sports and as a result, sports have become a crucial and essential part of human life. Today, sports have formed a huge industry of social economy and has attracted many interested parties, such as players, spectators, sport clubs, enterprises, government and social organizations [4], [5], [6]. As a result, the healthy and green running of sports industry is critical to the continuous progress of a whole country, or a nation, even all the world.

However, the outbreak of COVID-19 has brought a series of difficulties for the continuous development of sports industry [7], [8], [9], [10]. For example, in the COVID-19 environment, people’s demands to sport goods are often fluctuant with time: when the pandemic condition around is improving, people are very confident to gain better health and therefore, people’s attitude towards sport goods is positive; on the contrary, when the pandemic condition around is getting worse, people have no much interests in sports and as a consequence, sport goods are not welcome any more. Therefore, current sports market is not stable. As a result, it is becoming a practical and valuable task to accurately predict the sports industry scale, because the accurate scale prediction can provide a good guidance and reference to the production and consumption of sport goods.

However, the above sports industry scale prediction task is often confronted with three challenges. First of all, sports have formed a huge industry with many sport goods [11]; as a result, it is hard to quickly predict their future production or consumption scales accurately. Second, for a certain sport commodity, its production or consumption scale is often related to time especially in the COVID-19 environment; while the time-aware prediction is often hard to make due to the dynamic property [12], [13], [14]. Third, sports industry scale data often contain some privacy of users, which probably disables data stakeholders to disclose their sensitive data [15], [16], [17]; in this situation, the sparse data further blocks the accurate prediction of future production or consumption scales of sport goods.

In view of these three challenges, a novel Sports Industry Scale Prediction approach (named SISP) is proposed for healthcare, which is basically according to the time series analysis of historical sports industry scale data. Through SISP approach, we can quickly and accurately predict the future production or consumption scales of sport goods, in a privacy-aware way. At last, we prove the effectiveness and efficiency of the proposed SISP approach, through a set of experiments designed on a time series dataset.

In general, the major contributions of this work are summarized one by one.

(1) We model the sports industry scale prediction problem in COVID-19 environment into a time series analysis or a missing value prediction problem associated with historical production or consumption data of various sport goods.

(2) We introduce time-aware Locality-Sensitive Hashing (LSH) technique [18], [19] into the time series analysis and missing value prediction problem, and then put forward a novel sports industry scale prediction approach, i.e., SISP that is not only time-efficient but also privacy-preserving.

(3) Experiments are used to validate the feasibility of SISP approach, which are on real-world time series dataset WS-DREAM [20]. Reported experiment data prove the time efficiency and feasibility of the proposal.

The reminder of the article is structured. Related literatures published in recent years have been investigated and summarized in Section 2. A concrete example is designed in Section 3. Concrete steps of our proposed SISP approach are clarified in detail in Section 4. Experiment comparisons and evaluations are made in Section 5. At last, we summarize this article and discuss the possible research directions in Section 6.

2. Related literature

Here, we summarize the existing research literature of sports industry scale prediction for healthcare. In concrete, we summarize the recent cutting-edge research outcomes from the following four aspects.

2.1. Influencing factors of consumers’ purchase intention

In [21], the authors introduce OIT, VBN and GST theories to investigate the impact of environmental factors. The innovation of this article is to study the influence of environmental incentive factors on consumers’ willingness to buy environmentally friendly sportswear. This study shows that environmental attitude, concern, responsibility and peer influence are positively correlated with good purchasing habit, and individual green value has a moderating effect on peer influence and green purchasing behavior. In [22], MGB is used to explore consumers’ behavioral intentions of purchasing sports goods online. It is found that consumers’ attitudes, subjective norms, positive and negative expected emotions have important influence on their buying behaviors. It is noted that male users’ desire has a much higher impact on behavior than female users. Through this study, we can clearly see consumers’ willingness to buy sports goods online and the gender differences in their decision-making process.

2.2. Consumer shopping loyalty prediction

In [23], the authors aim to analyze the effect of brand perceived quality on honesty and attitude. In this research, structural equation model (SEM) is used to establish the data structure model. This study shows that perceived quality has a certain effect on credibility, but it has no effect on attitude. Honesty and attitude have influence on loyalty, but attitude has no direct impact on word of mouth. Through the research, sports service industry can be managed more efficiently with less resource expenditure, so that it can plan appropriate activities more accurately.

In [24], the purpose is to explore if consumers’ perception of corporate social responsibility (CSR) could infer behavioral loyalty. The author tested the relationship between perceived CSR and behavioral loyalty through objective data, and found that there was a positive mediating correlation between CSR and loyalty. Meanwhile, it is found positive significance of CSR initiative to behavioral loyalty also depends on the specific psychological state activated by consumers’ perception of such initiative.

2.3. Construction of evaluation system and prediction model

In [25], the authors analyze health criterion of sport goods, and on this basis, establishes the health evaluation model of sports goods, and discusses the status quo and upcoming development direction of normal development of their nation’s sports goods according to the model. Reports indicate healthy development of the sports goods in China shows a trend of gradual increment, while practical development of sports goods as well as its supporting environment generally indicate a trend of stable rising.

In [26], for supporting the coordinated development of sports industry, this paper constructs an evaluation model for the coupling coordinated development of sports industry. At the same time, entropy method and coupling coordination model are employed to mine the existing data, and the coupling coordination relationship between sports and pension industry is discussed.

In [27], a simulation model is established and tested to predict gaze hit under dynamic market stimulus. Based on the neural network of eye movement tracking, this model can accurately predict the fixation behavior, and it is found that visual attention is related to the low-level important characteristics of dynamic brand stimuli. This study mainly emphasizes the significance of eye-movement behaviors for prior evaluation of visual communication stimuli. This is beneficial to marketing in various aspects.

In [28], this paper proposes a new method to evaluate financial performance combining financial effectiveness and efficiency. The main innovation of this method lies in the addition of research, theory and practitioner perspectives and the use of binary logistic regression as well as Kendall Tau correlation to analyze data. The results show that the project service in the proposal is better at time efficiency than effectiveness.

2.4. Construction of evaluation system and prediction model

In [29], the purpose is to explore the influence of social support, career belief and career self-efficacy on the career development. Concretely, structural equation is recruited to establish the model and complete the statistical analysis of the data. The results show that occupational belief has a positive effect on the prediction of career self-efficacy and career development. Social support has a positive effect on the prediction of occupational belief and occupational self-efficacy.

In [30], the authors provide a comprehensive understanding of social live streaming services (SLSSs) in the context of sports, discuss the expected satisfaction of sports fans watching sports events using SLSSs, and also consider the key role of identity in the correlation between emotional satisfaction and flow when utilizing SLSSs. The four types of expected satisfaction were found to enhance flow and satisfaction, thus increasing social well-being and reducing loneliness. The authors offer proof and new insights for improving the understanding of sports content SLSSs.

2.5. Privacy protection and data sparsity

To secure the sensitive information contained in big data, many researchers have devoted themselves to investigating various privacy protection solutions, such as threshold-based privacy protection [31], [32], [33], differential privacy [34], [35], [36], Simhash [37], [38], and so on. In addition, data sparsity is inevitable in the big data environment [39], [40], [41]. To tackle this issue, many researchers have proposed various resolutions based on machine learning, e.g., Graph Neural Network [42], [43], Attention Mechanism [44], Approximate Nearest Neighbor search [45], Deep Correlation Mining [46] and LSTM [47]. Due to the page limit of the paper, we will not introduce their details one by one here.

3. Motivation

To ease the understanding of readers and highlight the research background and significance of this paper, we provide a motivating example in Fig. 1. The figure presents a series of sport goods as well as their production or consumption amount with time elapsing. For example, at time point $T_1$, three kinds of sport goods are produced, i.e., {glove, flag, shirt} and their sale volumes are 100, 200 and 300, respectively; at time point $T_2$, three kinds of sport goods are produced, i.e., {flag, shirt, volleyball} and their sale volumes are 900, 800 and 600, respectively, and so on.

Fig. 1.

Time-aware sale volumes variation of different sport goods: an example.

With such historical sale data of sport goods, we can analyze and predict the future sale volume of all sport goods at a certain time point. However, such a prediction is often confronted with three challenges: (1) There are many sport goods or commodities whose sale volumes at a certain time point need to be predicted, which is often time-consuming due to the heavy computation cost; (2) For each sport commodity, its sale volume is heavily influenced by the time factor, which makes it hard to accurately predict its future sale volume in a dynamic manner; (3) The historical sale volume data of sport goods or commodities are a kind of private information, which makes it hard to persuade data stakeholders to release their recorded sale volumes to public; in this situation, the available sport goods sale data are probably very sparse and as a consequence, the accurate prediction becomes more difficult.

To address these issues, we propose a novel sports industry scale prediction approach for healthcare based on time series analysis, i.e., SISP. Steps of SISP approach is specified in next section.

4. Time series analysis based sport goods sale volume prediction

Next, we describe the concrete steps of the sport goods sale volume prediction approach based on time series analysis, i.e., SISP approach. In general, SISP approach mainly includes the following three steps presented in Fig. 2. Moreover, the framework of the proposed SISP approach is presented in Fig. 3 where the relationships of the three steps of SISP are clarified clearly.

Fig. 2.

Main procedure of the proposed SISP approach.

Fig. 3.

Framework of our proposed SISP approach.

Step 1: Time point index creation.

According to the motivating example in Fig. 1, each sport commodity’s sale volume is varied with time. Therefore, the historical sale volume data of all sport goods can be formally described with a matrix $S$ presented in (1). Here, we assume that there are totally $N$ sport commodities $c_1,\dots ,c_N$, and $M$ time points $t_1,\dots ,t_M$; $s_{i,j}$ indicates the sale volume of sport commodity $c_j(1\le j\le N)$ at time point $t_i(1\le i\le M)$. Thus, $S$ in (1) is an $M\ast N$ matrix. Please note that if sport commodity $c_j$ has no sale volume at time point $t_i$, then the corresponding sale volume $s_{i,j}$ in matrix $S$ would be equal to zero, i.e., $s_{i,j}=0$. This means that such sale volume data would not take part in the subsequent calculation process including index creation, clustering and prediction.

$$S=\begin{array}{cc}\hfill \hfill & \hfill \begin{array}{ccc}\hfill c_1\hfill & \hfill \cdots \hfill & \hfill c_N\hfill \end{array}\hfill \\ \hfill \begin{array}{c}\hfill t_1\hfill \\ \hfill ⋮\hfill \\ \hfill t_M\hfill \end{array}\hfill & \hfill \left[\begin{array}{ccc}\hfill s_{1,1}\hfill & \hfill \cdots \hfill & \hfill s_{1,N}\hfill \\ \hfill ⋮\hfill & \hfill \ddots \hfill & \hfill ⋮\hfill \\ \hfill s_{M,1}\hfill & \hfill \cdots \hfill & \hfill s_{M,N}\hfill \end{array}\right]\hfill \end{array}$$ (1)

In matrix $S$, each column denotes a sport commodity’s historical sale volumes at $M$ time points $t_1,\dots ,t_M$; while each row represents $N$ sport commodities $(c_1,\dots ,c_N)$’ historical sale volumes at a certain time point. For example, the first row $s_1(s_{1,1},\dots ,s_{1,N})$ indicates $N$ sport commodities $(c_1,\dots ,c_N)$’ historical sale volumes at $t_1$. Next, we create an index for each time point $t_1,\dots ,t_M$ according to the sale volumes of sport goods in matrix $S$.

In concrete, we produce an $N$-dimensional vector $V=(v_1,\dots ,v_N)$ according to the generation rule in (2). Here, $v_j$ is a random value between −1 and 1, which is based on the rationale of LSH (in concrete, for continuous data, their corresponding projection vector’s entry value should belong to the range (−1, 1)). Next, a dot product operation is made executed between vector $V$ and vector $s_i$ corresponding to the time point $t_i$ $(1\le i\le M)$, which is formalized in Eq. (3). Here, $scor{e}_i$ is either positive or negative or zero. Next, we take a conversion operation for $scor{e}_i$, whose results (denoted by ${}^{\#}scor{e}_i$) are presented in (4). As (4) shows, ${}^{\#}scor{e}_i$ is a binary value.

$$v_j=rand(-1,1)(1\le j\le N)$$ (2)

$${\mathit{score}}_i=s_i\cdot V=\sum _{j=1}^N{S}_{i,j}\ast v_j$$ (3)

$${}^{\#}{\mathit{score}}_i=\left\{\begin{array}{cc}1\hfill & \text{ if }{\mathit{score}}_i>0\hfill \\ 0\hfill & \text{ if }{\mathit{score}}_i\le 0\hfill \end{array}\right)$$ (4)

With the Eqs. (2)–(4), we can transform the original vector $s_i$ which is sensitive enough into a Boolean value ${}^{\#}scor{e}_i$, i.e., a hashing mapping from $s_i$ to ${}^{\#}scor{e}_i$, i.e., $s_i{\to }^{\#}scor{e}_i$ is achieved. However, such a mapping is not sufficient because LSH is probability-based. Therefore, for vector $s_i$, we execute the operations in (2)–(4) multiple times (here, we assume $P$ times) and then we get $P$ score values: ${}^{\#}scor{e}_{i,1}$, …, ${}^{\#}scor{e}_{i,P}$. In other words, we get a vector $h_i=({}^{\#}scor{e}_{i,1},\dots {,}^{\#}scor{e}_{i,P})$ and a new mapping from $s_i$ to $h_i$, i.e., $s_i\to h_i$. Here, we regard $h_i$ as the index for $s_i$ as well as the corresponding time point $t_i$. And all the mappings from time points to their index values form a hash table (see Table 1). Since $h_i$ is a Boolean vector which does not contain much private information of sport goods sale data, we use $h_i$ in the rest steps to achieve the goal of privacy-aware sport goods production and consumption volume prediction.

Table 1.

A hash table $T$ of $M$ time points.

Time point	Index value	Mapping
$t_1$	$h_1$	$t_1\to h_1$
$t_2$	$h_2$	$t_2\to h_2$
…	…	…
$t_M$	$h_M$	$t_M\to h_M$

Step 2: Similar time points finding.

In the previous step, we have created the index for each time point $t_i(1\le i\le M)$, i.e., $h_i$. Then generally, according to LSH rationale, we can evaluate whether two time points are similar according to their respective index values. More specifically, considering two time points $t_i$ and $t_j$: if their index values are equal, i.e., $h_i=h_j$, then we can approximately regard $t_i$ and $t_j$ are similar. However, as we analyzed previously, LSH is probability-based; therefore, condition $h_i=h_j$ cannot be directly used to evaluate if $t_i$ and $t_j$ are similar. More specifically, the judgment condition $h_i=h_j$ is often too strict for similar time points evaluation. In this situation, we need to find out more effective relaxation ways to loosen the evaluation condition $h_i=h_j$.

Concretely, we repeat the hash table creation process (see Step 1 and Table 1) $Q$ times and then we get $Q$ hash tables, i.e., $T_1,\dots ,T_Q$ as illustrated in Table 2. As Table 2 shows, each time point is corresponding to $Q$ index values; for example, $t_1$ has $Q$ index values: $h_{1,1},h_{2,1},\dots ,h_{Q,1}$. Then we can relax the time point evaluation condition $h_i=h_j$ with the new evaluation condition presented in (5). More intuitively, if there exists a hash table $T_z$ among $\{T_1,\dots ,T_Q\}$ in which $h_{z,i}=h_{z,j}$ holds, then we can approximately regard the time points $t_i$ and $t_j$ are similar. In other words, we do not require that $t_i$ and $t_j$ are similar in all hash tables. This way, the similar time point evaluation condition is relaxed significantly. Then according to the similar relationships between different time points, we can divide all the $M$ time points $t_1,\dots ,t_M$ into different clusters.

Table 2.

$Q$ hash tables (i.e., $T_1,\dots ,T_Q$) of $M$ time points.

Time point	Index value
	$T_1$	$T_2$	...	$T_Q$
$t_1$	$h_{1,1}$	$h_{2,1}$	…	$h_{Q,1}$
$t_2$	$h_{1,2}$	$h_{2,2}$	…	$h_{Q,2}$
…	…	…	…	…
$t_M$	$h_{1,M}$	$h_{2,M}$	…	$h_{Q,M}$

Here, please note that we adopt classical LSH technique to search for similar time points while LSH has been proven a time-efficient search technique with a small time complexity of O(1). Therefore, we can guarantee high sport goods sale prediction efficiency.

$$t_i\text{ and }{t}_j\text{ are similar, iff }{h}_{z,i}=h_{z,j}\text{ for any }z=1,2,\dots ,Q$$ (5)

Step 3: Time-aware future sale volume prediction for sport goods.

In the previous steps, we have grouped the $M$ time points $t_1,\dots ,t_M$ into multiple clusters. Next, we use the similar time points as well as their sport goods sale data to predict the future sale volume of sport goods at a certain time point. The prediction rationale is direct and simple, i.e., if two time points are similar (i.e., they belong to an identical cluster), then their sale volume data of sport goods are close. This way, we can predict the future sale volume of the sport commodity $c_j(1\le j\le N)$ at a target time point $t_i(1\le i\le M)$, i.e., $s_{i,j}$ according to the formula in (6). Here, $Se{t}_i$ denotes the cluster which contains the time point $t_i$. This way, we can predict the unknown $s_{i,j}$ value accurately. Moreover, since the prediction is based on privacy-less index values of $M$ time points, we can conclude that our proposed SISP approach can accurately predict the future sale volumes of sport goods while securing the sensitive information of sale data stakeholders.

$$s_{i,j}=\frac{1}{\left|Se{t}_i\right|}\sum _{x\in Se{t}_i}{s}_{x,j}$$ (6)

For more formalization, the details of SISP approach can be described with Algorithm 1. Here, $t_{target}$ denotes the target time point at which we need to predict the sale volumes of sport goods or commodities, whose predicted result is taken as the algorithm output. Concretely, in Algorithm 1: line 1 is used to construct the time-aware sport goods sale matrix S; lines 2–13 are used to generate the index for each of the M time periods based on a single hashing process; lines 14–17 are used to generate a P-dimensional index for each time period; lines 18–21 are used to create a hash table; lines 22–25 are used to create Q hash tables; lines 26–32 are used to search for the similar time periods of a target time period; lines 33–40 are used to predict the future sales of target goods at a target time period.

5. Evaluation

Next, a set of experiments are deployed for testing the performances of SISP method. The recruited dataset is WS-DREAM which is a real-world dataset that records the time-related service performance data [30]. The dataset reflects the performance fluctuation condition of 4532 services at 64 time points. For validating the innovation of SISP, we compare our SISP approach with another two solutions, i.e., Partial-HR [48] and $SerRe{c}_{distri\text{-}LSH}$ [49]. Experiments are running on a laptop with 2.80 GHz processor and 16.0 GB RAM. The experiment running environment is Win-7 and Python 3.0. We run each set of experiments 50 times and finally we record their average performances.

Here, five sets of experiments are designed. Concretely, three parameters are involved: $M$ (volume of time points) $=$ 64, $N$ (volume of sport goods or commodities), $P$ (volume of hash functions), $Q$ (volume of hash tables), $D$ (threshold of volume of hash tables in which two time points are similar; typical value of $D$ is 1, see (5)).

(1) Profile 1: Accuracy of three approaches.

In this profile, we measure the prediction accuracy (i.e., RMSE, smaller is better) of the SISP approach by comparing it with Partial-HR and $SerRe{c}_{distri\text{-}LSH}$. Concretely, parameter setting is as follows: $N$ is varied from 500 to 4000, $D$ $=$ 1, $P$ $=$ 2 and $Q$ $=$ 10. Comparison results are shown in Fig. 4. The compared results show that SISP approach achieves a better prediction accuracy compared to the other approaches because the RMSE of SISP is lower than the RMSE of Partial-HR and $SerRe{c}_{distri\text{-}LSH}$. This can be explained as follows: SISP adopts time-aware LSH technique which can filter out the really similar time points and then use them to make accurate prediction. Another observation from Fig. 4 is that the prediction accuracy of Partial-HR increases with the growth of $N$ while the prediction accuracy values of the rest two approaches are relatively stable with the increment of $N$.

Fig. 4.

RMSE of three approaches w.r.t $N$.

(2) Profile 2: Time costs of three approaches.

We test and compare the time efficiency of the three approaches in this profile. Concrete parameters setting is: $N$ changes between 500 and 4000, $D$ $=$ 1, $P$ $=$ 2 and $Q$ $=$ 10. Comparison results are reported in Fig. 5. The report indicates that efficiency of Partial-HR is much smaller than those of SISP and $SerRe{c}_{distri\text{-}LSH}$ because there is no complex computation regarding similar time points finding in both SISP and $SerRe{c}_{distri\text{-}LSH}$. However, the time cost of SISP approach is generally small and acceptable in most situations. Another observation from Fig. 5 is that the time cost of three approaches all increase with the growth of $N$, which is due to the fact that more sport commodities often means a larger matrix $S$ in (1) and as a consequence, more computational time is needed to make accurate prediction based on the larger matrix $S$.

Fig. 5.

Time cost of three approaches w.r.t $N$.

(3) Profile 3: Accuracy of SISP with respect to threshold $D$

As analyzed in the algorithm part, the parameter $D$ has a direct influence towards the accuracy of SISP approach. To observe this correlation, we design a set of experiments to measure the accuracy of SISP with respect to parameter $D$. Experiment parameter settings are as follows: $N$ $=$ 4000, $D$ changes between 2 and 8, $P$ $=$ 2, $Q$ $=$ 10. Comparison results are reported in Fig. 6. Report indicates the variation tendency of RMSE of SISP approach with respect to parameter $D$. As indicated in Fig. 6, RMSE value of SISP decreases approximately with the rising of $D$. This can be explained as follows: parameter $D$ denotes the strength of constraint condition for evaluating if two time points are similar according to (5) and a larger $D$ indicates the constraint condition is very strict and therefore, the returned similar time points are “very similar”. In this situation, the prediction accuracy is very high and correspondingly, RMSE value is very low, vice versa.

Fig. 6.

RMSE of SISP w.r.t $D$.

(4) Profile 4: Time cost of SISP with respect to threshold $D$

Similar to Profile 3, the algorithm’s efficiency of SISP approach is also correlated with parameter $D$. To observe such a correlation, we have designed a set of experiments for investigating the correlation between time cost of SISP approach and $D$ value. Concrete experiment parameter settings are as follows: $N$ $=$ 4000, $D$ changes between 2 and 8, $P$ $=$ 2, $Q$ $=$ 10. Comparison results are reported in Fig. 7.

Fig. 7.

Time cost of SISP w.r.t $D$.

As Fig. 7 shows, time cost of SISP decreases when $D$ increases. This is due to the fact that: parameter $D$ denotes the strength of constraint condition for evaluating if two time points are similar according to (5) and a larger $D$ indicates the constraint condition is very strict and therefore, the returned similar time points are “very similar”. In this situation, only a small number of similar time points are returned for subsequent prediction and therefore, less computation load is incurred. As a result, time cost is decreased accordingly.

(5) Profile 5: Convergence of SISP.

We test the performance convergence of SISP approach whose variation tendency is presented in Fig. 8. Experiment results prove the performances of SISP is convergent with the number of experiment times increases, in terms of RMSE and time efficiency. Therefore, it validates the reasonability that we repeat each set of experiments 50 times and finally adopt their average value for reports and demonstration.

Fig. 8.

Performance convergence of SISP approach.

6. Conclusions

With the advent of COVID-19, sports industry has been influenced significantly and the industry scale is decreased considerably. In this situation, how to accurately predict the sports industry scale in terms of production and consumption is becoming a practical and valuable task. However, three challenges are often existing in the sports industry scale prediction. First of all, there are so many kinds of sport goods that it is hard to predict their future production or consumption scales quickly. Second, production or consumption scale of sport goods is often fluctuant and hard to predict accurately especially in the COVID-19 environment. Third, sports industry scale data often sensitive enough. In view of these three challenges, a novel sports industry scale prediction approach SISP is proposed for healthcare in this paper, which is mainly based on time series analysis. Through SISP, we can quickly and accurately predict the future production or consumption scales of sport goods in a privacy-aware way. At last, we validate the effectiveness and efficiency of SISP approach via a set of experiments on WS-DREAM dataset.

However, influenced by the inherent nature of LSH, privacy protection effect of SISP approach is difficult to measure directly. In the future, we will study how to introduce more effective and quantified privacy protection strategies into SISP. In addition, we will make full use of advanced artificial intelligence technologies to investigate more robust prediction algorithm by considering data sparsity even cold-start issues. Besides, goods sale prediction issue is often related to many influencing factors (e.g., sale location); therefore, we will further discuss the more complex prediction problem with multiple dimensions in the future study.

Funding

This paper was supported by the Ministry of Education in China Project of Humanities and Social Sciences (No. 18YJA890013).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.