Extracting the multi-timescale activity patterns of online financial markets

Kobayashi, Teruyoshi; Sapienza, Anna; Ferrara, Emilio

doi:10.1038/s41598-018-29537-w

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…The fifth data set, “Interbank”, is a temporal financial network in which nodes and edges represent banks and overnight lending–borrowing relationships, respectively. Since overnight loan contracts last only for 1 day, we can construct a sequence of daily snapshot networks (i.e., time resolution is 1 day) 29,30 . We consider here the data on the online interbank market in Italy, called e-MID, between June 12, 2007 and July 9, 2007 (i.e., 20 business days).…”

Section: Resultsmentioning

confidence: 99%

The structured backbone of temporal social ties

Kobayashi

Takaguchi²,

Barrat

2019

Nat Commun

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In many data sets, information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections is a non-trivial task. Moreover, mehods put forward until now do not deal with time-resolved network data, which have become increasingly available. Here we develop a method for filtering temporal network data, by defining an adequate temporal null model that allows us to identify pairs of nodes having more interactions than expected given their activities: the significant ties. Moreover, our method can assign a significance to complex structures such as triads of simultaneous interactions, an impossible task for methods based on static representations. Our results hint at ways to represent temporal networks for use in data-driven models.

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Section: Resultsmentioning

confidence: 99%

The structured backbone of temporal social ties

Kobayashi

Takaguchi²,

Barrat

2019

Nat Commun

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“…We represent consumers' expenditure data as a 3-way tensor, which will be detailed in the following section. NTF is widely used to mine temporal patterns in face-to-face contacts [9,10], financial transactions [14], online communications [11] and online games [12]. Based on the decomposed patterns from our consumption data, we show that consumers with different demographics have different consumption patterns.…”

Section: Related Workmentioning

confidence: 92%

“…To pursue this goal, we use a non-negative tensor factorization (NTF) to obtain the latent factors that would reflect the characteristic expenditure patterns across different attributes of consumers [7][8][9]12]. Here, we try to extract multi-timescale patterns that would exist at intra-and inter-week scales [14]. We represent the users' shopping records by a 3-way tensor, whose size is given by I × J × K , where I =#consumers ( = 2624 ), J =#days in a week ( = 7 ) and K =#weeks ( = 42 ).…”

Section: Tensor Representation Of Consumption Expenditurementioning

confidence: 99%

“…The NTF method has been widely used to mine temporal patterns in different social contexts, such as face-to-face contacts among humans [9,10], online communications [11], online game [12] and students' life in a university [13]. However, mining multi-timescale patterns has not been done so far, except for the study uncovering the intra-and inter-day transaction patterns of banks [14].…”

Section: Introductionmentioning

confidence: 99%

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Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

et al. 2020

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Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers' behavior need to be considered, such as consumers' demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intraand inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.

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“…The fifth data set, "Interbank", is a temporal financial network in which nodes and edges represent banks and overnight lending-borrowing relationships, respectively. Since overnight loan contracts last only for one day, we can construct a sequence of daily snapshot networks (i.e., time resolution is one day) 30,31 . We consider here the data on the online interbank market in Italy, called e-MID, between June 12, 2007 and July 9, 2007 (i.e., 20 business days).…”

Section: Datamentioning

confidence: 99%

The structured backbone of temporal social ties

Kobayashi,

Takaguchi,

Barrat

2018

Preprint

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In many data sets, crucial information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections, the "network backbone", is a non-trivial task, and methods put forward until now do not address time-resolved networks, whose availability has strongly increased in recent years. We develop here such a method, by defining an adequate temporal network null model, which calculates the random chance of nodes to be connected at any time after controlling for their activity. This allows us to identify, at any level of statistical significance, pairs of nodes that have more interactions than expected given their activities: These form a backbone of significant ties. We apply our method to empirical temporal networks of socio-economic interest and find that (i) at given level of statistical significance, our method identifies more significant ties than methods considering temporally aggregated networks, and (ii) when a community structure is present, most significant ties are intra-community edges, suggesting that the weights of inter-community edges can be explained by the null model of random interactions. Most importantly, our filtering method can assign a significance to more complex structures such as triads of simultaneous interactions, while methods based on static representations are by construction unable to do so. Strikingly, we uncover that significant triads are not equivalent to triangles composed by three significant edges. Our results hint at new ways to represent temporal networks for use in data-driven models and in anonymity-preserving ways.

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Extracting the multi-timescale activity patterns of online financial markets

Cited by 6 publications

References 42 publications

The structured backbone of temporal social ties

The structured backbone of temporal social ties

Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

The structured backbone of temporal social ties

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