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Mi ning Frequent Episodes for relating Financial Events and Stock Trends. Anny Ng and Ada Wai-chee Fu PAKDD 2003 報告者: Ming Jing Tsai. Definition. Events : financial news ,political … e 1 ,e 2 ,e 3 … .,e k : event types day record D i :{e i1 ,e i2 ,e i3 … .,e ik }

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Mi ning Frequent Episodes for relating Financial Events and Stock Trends

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Mi ning frequent episodes for relating financial events and stock trends

Mining Frequent Episodes for relating Financial Events and Stock Trends

Anny Ng and Ada Wai-chee Fu PAKDD 2003

報告者:Ming Jing Tsai

date:2004/03/05


Definition

Definition

  • Events : financial news ,political…

  • e1,e2,e3….,ek : event types

  • day record Di:{ei1,ei2,ei3….,eik}

  • Episode:{e1,e2,e3….,ek},has at least two elements and at least one ej is a stock event type

  • Window = x days


Definition1

Definition

  • Window frequency:number of windows that contains an event type

  • DB frequency:number of occurrences of an event type in DB

  • Frequency of an episode (ex)

    • number of windows

    • the first day of window contains at least one of the event types in episode.


Construct event tree

Header in descending db frequencies order

Event_set pair <(firstday) ,(remaining day)>

sorted in the descending db frequencies

node<E:C:B>:

E :event type ,c :counts ,b :binary bit

Construct event tree


Pruning method

Pruning method

  • window frequencies < min_sup

  • Remove duplicate event type in both firstday part and remaining day part


Mi ning frequent episodes for relating financial events and stock trends

An Event database

Window = 3,min_sup =3

Db frequencies<a:2,b:3,c:2,d:2>


Windows

windows

Window = 3,min_sup =3

Ordered frequent event type<b,a,c,d>

Window frequencies<a:5,b:5,c:5,d:6>


Mi ning frequent episodes for relating financial events and stock trends

{null}

{b:1:0}

{a:1:0}

{a:1:1}

{c:1:0}

{c:1:1}

{b:1:1}

{d:1:1}


Mi ning frequent episodes for relating financial events and stock trends

{null}

{b:2:0}

{a:1:0}

{d:1:0}

{a:1:1}

{c:1:0}

{b:1:1}

{d:1:1}

{c:1:1}

{b:1:1}

{a:1:1}

{d:1:1}

{c:1:1}


Mi ning frequent episodes for relating financial events and stock trends

{null}

{b:3:0}

{a:1:0}

{d:1:0}

{a:1:1}

{c:1:0}

{b:1:1}

{d:1:1}

{c:1:1}

{b:1:1}

{a:1:1}

{d:1:1}

{c:1:1}


Mi ning frequent episodes for relating financial events and stock trends

{null}

{b:3:0}

{a:1:0}

{d:1:0}

{a:2:1}

{c:1:0}

{b:1:1}

{d:1:1}

{c:2:1}

{b:1:1}

{a:1:1}

{d:1:1}

{d:1:1}

{c:1:1}


Mi ning frequent episodes for relating financial events and stock trends

{null}

{b:3:0}

{a:2:0}

{d:1:0}

{a:2:1}

{c:2:0}

{b:1:1}

{d:1:1}

{c:2:1}

{b:1:1}

{a:1:1}

{d:1:1}

{d:1:1}

{d:1:1}

{c:1:1}


Mi ning frequent episodes for relating financial events and stock trends

{null}

{b:3:0}

{a:2:0}

{d:2:0}

{a:2:1}

{c:2:0}

{b:1:1}

{d:1:1}

{c:2:1}

{b:1:1}

{a:1:1}

{d:1:1}

{d:1:1}

{d:1:1}

{c:1:1}


Mining frequent episode

Mining frequent episode

  • Header table{h0,h1,…..,hH}

  • Mining recursively each of the linked list kept at the header table

    • from bottom to top

  • Conditional path can build conditional event tree

  • Object 1:found frequent episodes of form {a} ∪{hi}

    • first-part frequencies

  • Object 2:found frequent episodes that contain hi and at least two other event types

    • Db frequencies


Traverse conditional path

Traverse conditional path

  • Remove invalid event types

  • Adjust counts of nodes above hi in the path to be equal to that of hi

  • If hi is in the firstdays part, then move all event types in the remainingdays part to the firstdays part

  • Remove hi from the path


Generate frequent episode

Generate frequent episode

  • When a conditional event tree contains only a single path

    • Any subset of firstpart ∪ event base set

    • Any Subsets of firstpart ∪ Any Subsets of remainingpart ∪ event base set


Mining header d

Mining Header d

min_sup =3

event base set {d}

  • <(a:1,c:1),(b:1)>

  • <(b:1),()>

  • <(b:1,a:1,c:1),()>

  • <(b:1),(a:1,c:1)>

  • <(a:1,c:1),()>

db frequency:{<b:4,a:4,c:4>}

First_part frequency:{<b:3,a:3,c:3>}

Frequent episode :{bd,ad,cd}


Recursively mining header c

Recursively Mining Header c

event base set {cd}

  • <(a:1,b:1),()>

  • <(b:1,a:1),()>

  • <(b:1),(a:1)>

  • <(a:1),()>

<(a:1,c:1),(b:1)>

<(b:1),()>

<(b:1,a:1,c:1),()>

<(b:1),(a:1,c:1)>

<(a:1,c:1),()>

db frequency:{<b:3,a:4>}

First_part frequency:{<b:3,a:3>}

Frequent episode :{bcd ,acd}


Mi ning frequent episodes for relating financial events and stock trends

Recursively Mining Header a

event base set {acd}

  • <(b:1),()>

  • <(b:1),()>

  • <(b:1),()>

<(a:1,b:1),()>

<(b:1,a:1),()>

<(b:1),(a:1)>

<(a:1),()>

db frequency:{<b:3>}

First_part frequency:{<b:3>}

Frequent episode :{bacd}


Mi ning frequent episodes for relating financial events and stock trends

Mining Header c

min_sup =3

event base set {c}

  • <(b:1),(a:1)>

  • <(a:1,b:1),()>

  • <(b:1),(a:1)>

  • <(a:1),()>

db frequency:{<b:3,a:4>}

First_part frequency:{<b:3,a:2>}

Frequent episode :{bc}


Mi ning frequent episodes for relating financial events and stock trends

Recursively Mining Header a

min_sup =3

event base set {ac}

  • <(b:1),()>

  • <(b:1),()>

  • <(b:1),()>

db frequency:{<b:3>}

First_part frequency:{<b:3>}

Frequent episode :{bac}


Mi ning frequent episodes for relating financial events and stock trends

Mining Header a

min_sup =3

event base set {a}

  • <(b:1),()>

  • <(b:1),()>

  • <(b:1),()>

db frequency:{<b:3>}

First_part frequency:{<b:3>}

Frequent episode :{ba}


Experiment synthetic data

Experiment (synthetic data)


Dataset 2 t20 i5 m1000 d3k

Dataset 2 T20,I5,M1000,D3K


Experiment real data

Experiment (real data)

  • News event from a internet

    • 121 event types

    • 757 days

  • Stock data

    • Dow Jones ,Nasdaq ,Hang Seng , 12 top local companies


Experiment real data1

Experiment (real data)


Experiment real data2

Experiment (real data)


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