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I'm working on a dataset of banking transactions and would like to find recurrent transactions. | |
I've been mapping transactions per merchant in timeseries, and tried to use acf from statsmodels.tsa.stattools to calculate the autocorrelation function but i'm not getting the expected results: | |
`r = acf(ts, fft=False)` | |
For example this set of transaction (ASSURANCE DESJ) is getting an acf score of 0.3159 when it's obviously a recurring transaction (same amount, same frequency). | |
[![enter image description here][1]][1] | |
Another example of recurring transactions with acf=0.22775: |
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function showthemess() | |
{ | |
$.ajax({ | |
type: 'GET', | |
url: '/tweets/'+ id, | |
data: {'last_tweet' : lasttweet}, | |
success: function(data){ | |
lasttweet = data.last_tweet; | |
$(function () { | |
$("#tweetspanel") |
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rtmpdump -i "rtmp://ams.server.com/vod playpath=mp4:myfolder/myfile.mp4" -o ~/test.flv | |
RTMPDump v2.4 | |
(c) 2010 Andrej Stepanchuk, Howard Chu, The Flvstreamer Team; license: GPL | |
Connecting ... | |
WARNING: HandShake: client signature does not match! | |
INFO: Connected... | |
Starting download at: 0.000 kB | |
INFO: Metadata: | |
INFO: duration 3299.36 | |
INFO: moovPosition 254621187.00 |
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curl -XGET http://127.0.0.1:19200/logstash-2012.12.12/apache/_search?pretty=1 -d '{ | |
"query" : { "match" : {"@fields.username" : "user1"}}, | |
"facets" : { "ip" : { "terms" : {"field" : "client"} } } | |
}' | |
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{ | |
"tweet" : { | |
"_source" : { | |
"compress" : true | |
}, | |
"properties" : { | |
"contributors" : { | |
"type" : "long", | |
"ignore_malformed" : false | |
}, |