Detect patterns σε μια γραμμη

παπι · 19 Σεπτεμβρίου 2014

Λεω να φτιαξω αλγοριθμους για signals σε μετοχες κλπ. Αλλα για να φτιαξω κατι τετοιο, πρωτα απ'ολα πρεπει να "ανιχνευω" καποια patterns. Το οποιο ειναι ευκολο να το κανεις με τον "εγκεφαλο" σου, αλλα "programmatically".... αγγουρι.

Για αρχη θελω να ανιχνευω καποια "υπαρκτα" patterns.

Πχ. εδω ειναι ενα 6μηνο γραφημα του οτε

Φαινεται random, αλλα στη πραγματικοτητα εχει 2 απο τα πιο επαναλαμβανομενα patterns, τα οποια δινουν short/long signals.

Το πρωτο ειναι το head and shoulders.

Το δευτερο ειναι το cup and handle

Το θεμα ειναι, πως τα βρισκουμε αυτα;

Directx · 19 Σεπτεμβρίου 2014

Μια πολύ γρήγορη σκέψη. Θα μπορούσες να μετρήσεις την διαφορά (διακύμανση) μεταξύ των σημείων που απαρτίζουν το γράφημα και βάση αυτής να εξαγάγεις κάποια χρήσιμα συμπεράσματα (κατηγοριοποίηση). Εννοείται ότι η σύγκριση των αποτελεσμάτων δεν θα ήταν απόλυτη αλλά ποσοστιαία (%). Το πότε μπορείς να θεωρήσεις ότι κάποιο γράφημα παρουσιάζει μεγάλες ομοιότητες με κάποια γνωστή κατηγορία μπορείς να το συμπεράνεις δημιουργώντας ένα προφίλ της διακύμανσης των σημείων που απαρτίζουν συνήθως τα γραφήματα κάθε κατηγορίας στο λογισμικό σου.

(?)

zynif · 19 Σεπτεμβρίου 2014

Mάλλον πρέπει να ψάξεις στο google για : data mining time series

Ένας αλγόριθμος που σου λέει πόσο μοιάζουν δύο χρονοσειρές είναι ο Dynamic Time Warping

akis_fc · 19 Σεπτεμβρίου 2014

Χωρίς να σου προτείνω βιβλιοθήκη θα σου πω πως το σκέφτηκα.

Έχοντας διαθέσιμους τους αριθμούς (υποθέτω):

Head&Shoulders

Από ότι βλέπω κάθε φορά που έχεις άνω άκρο ελέγχεις τα εξής αν το επόμενο άνω άκρο είναι πιο πάνω από το πρώτο αν το επόμενο κάτω είναι πιο κάτω από το πρώτο άνω και αν το επόμενο άνω είναι κάτω από το δεύτερο άνω. Πιο καθαρά

1-ανω-ακρο

επόμενο-κάτω-άκρο = 1-κάτω-άκρο

επομενο-ανω-ακρο = 2-ανω-ακρο > 1-ανω-ακρο

επόμενο-κατω-ακρο = 2-κατω-ακρο< 1-ανω-ακρο

επόμενο-άνω-άκρο = 3-άνω-άκρο < 2-άνω-άκρο && 3-άνω-άκρο > 1-κάτω-άκρο

Εννοείται πως όταν μιλάμε για άκρα το καταλαβαίνεις από την αλλαγή του προσήμου

Τώρα αν τυχόν θα πρέπει αυτές οι διαφορές να είναι συγκεκριμένες βάζεις μέσα ποσοστά. Όπως επίσης αν πρέπει να είναι εντός συγκεκριμένου χρονικού διαστήματος βάζεις κι έναν έλεγχο για το διάστημα. Με παρόμοιο τρόπο θα αντιμετώπιζα και το άλλο.

Κι όλα αυτά αν έχεις τα data σε αριθμούς.

Ενδιαφέρον project!

Technology fan · 19 Σεπτεμβρίου 2014

Mάλλον πρέπει να ψάξεις στο google για : data mining time series

Ένας αλγόριθμος που σου λέει πόσο μοιάζουν δύο χρονοσειρές είναι ο Dynamic Time Warping

Σορρυ, αλλά αυτό είναι άλλο πράγμα, Το Dynamic Time Warping είναι τεχνική με την οποία δύο χρονοσειρές διαφορετικού χρόνου μπορούν να συγκριθούν, οπότε "απλώνεις" τις χρονοσειρές σε ένα κοινό χρόνο hence "warping"...

Όσον αφορά το τοπικ, συνήθως σε μετοχές κάνουν πρόβλεψη τιμής χρησιμοποιώντας μοντέλα ARMA. Αυτά προσπαθούν να βρουν κάποια σχέση μεταξύ τιμών σε μια χρονοσειρά. Ένα απλό παράδειγμα είναι το εξής: φαντάσου λοιπόν αν μπορούσα να εκφράσω όλη την χρονοσειρά με κάποιους συντελεστές τύπου:

Χ0=α*Χ5+βΧ4+γ*Χ3+δ*Χ2+ε*Χ1

ή

Χ0=1,5*Χ1-0,5Χ2+0,3*Χ3-0,2*Χ4+0,1*Χ5

το οποίο σημαίνει οτι η τιμή μετοχής σήμερα (Χ0) ισούται με 1,5 επι την χθεσινή τιμή - 0,5 επί την προχθεσινή τιμή κτλπ για 5 μέρες πριν.

τα μοντέλα σαν τα ARMA αναλαμβάνουν να βρουν τους συντελεστές (α,β,γ,δ,ε) με βάση τις τιμές της χρονοσειράς που ήδη γνωρίζεις, μόνο που τα ARMA είναι πιο σύνθετα από αυτά που περιέγραψα, νομίζω έχουν συντελεστές και σε παρονομαστή δε θυμάμαι τώρα.

ψάξε αν θες για ARMA forecasting.

Μην ξαναεφεύρεις τον τροχο....

Επεξ/σία 19 Σεπτεμβρίου 2014 από Technology fan

brute-force · 19 Σεπτεμβρίου 2014

Tσέκαρε τον water filling algorithm, ίσως σου φανεί χρήσιμος.

http://qandwhat.apps.runkite.com/i-failed-a-twitter-interview/

https://en.wikipedia.org/wiki/Water_filling_algorithm

timonkaipumpa3 · 19 Σεπτεμβρίου 2014

Peak picking θες να κάνεις.

Και βάσει λόγων στα magnitudes των peaks και απόστασης στον χρόνο έχεις τα pattern σου.

mad-proffessor · 19 Σεπτεμβρίου 2014

Αμα θες να κάνεις τη διαφορά εδώ είσαι

http://papers.nips.cc/paper/2516-dynamical-modeling-with-kernels-for-nonlinear-time-series-prediction.pdf

NewProject · 19 Σεπτεμβρίου 2014

Γιατι δεν φτιάχνεις μια βάση δ. με γραφήματα (πατερνς) όπως έχεις κάνει με τις μαύρες και κόκκινες γραμμές και να συγκρίνεις την αρχική εικόνα με τη καθεμία απο τις εικόνες στη ΒΔ ;

παπι · 19 Σεπτεμβρίου 2014

Πραγματι. το peek picking το θελω.

Πραγματι. Δεν βρισκω κανα αλγοριθμο να το κανει..

ενα που βρικα μου βγαζει

10      9.885   Peek
10.2    9.871   Peek
10.47   9.877   Peek
10.75   9.946
10.7    10.067
10.95   10.196
10.99   10.358
10.9    10.472
10.95   10.546
10.88   10.621
10.92   10.679
10.7    10.771
10.9    10.821
11      10.864
11.11   10.889
11.18   10.93
10.91   10.953
11.05   10.945  Peek
11.31   10.96   Peek
11.26   10.996
11.35   11.034
11.1    11.077
11.35   11.117
11.07   11.162

Οποιος θελει να παιξει, εδω ενα test case

main.cpp

#include <iostream>
#include <vector>
#include <algorithm>
#include <list>
#include <numeric>
typedef struct
{
	time_t		time;
	float		high;
	float		low;
	float		open;
	float		close;
	size_t		volume;
}DataPoint;


DataPoint data[] = 
{
#include "data.array"
};


//http://stackoverflow.com/questions/12250134/peak-detection-in-time-series
void DetectPeeks(const std::vector<float>& input, std::vector<size_t>& idx)
{
	size_t count = input.size();
	float prev = -1.0f;
	float prevSlop = 0.0f;
	for(size_t i = 0; i < count; i++)
	{
		if( prev < 0.0f )
		{
			prev = input[i];
			continue;
		}
		float slop = input[i] - prev;
		if(slop * prevSlop < 0)
			idx.push_back(i);
		prevSlop = slop;
		prev = input[i];
	}
}
void CreateSMA(const std::vector<float>& input, std::vector<float>& output, size_t val)
{
	size_t count = input.size() - val;
	output.resize(val,0.0f);
	
	std::list<float> sum(input.begin(),input.begin() + val);
	for(size_t i = 0; i < count; i++)
	{
		float sum = std::accumulate(input.begin() + i,input.begin() + i + val,0.0f);
		output.push_back(sum/val);
	}
}

int main()
{
	std::vector<float> prices;
	//get close values
	for(size_t i = 0; i < _countof(data); i++)
		prices.push_back(data[i].close);

	std::vector<size_t> peeks;

	//create sma
	std::vector<float> sma;
	CreateSMA(prices,sma,10);

	//detect peeks
	DetectPeeks(sma,peeks);
	std::reverse(peeks.begin(),peeks.end());

	
	for(size_t i = 0; i < sma.size(); i++)
	{
		std::cout<<prices[i]<<"\t"<<sma[i];
		if( !peeks.empty() && peeks.back() == i)
		{
			std::cout<<"\tPeek";
			peeks.pop_back();
		}
		std::cout<<std::endl;
	}
	return 0;
}

data.array

{ 1253547337, 11.650000f, 11.430000f, 11.600000f, 11.450000f, 920728 },
{ 1253633737, 11.550000f, 11.140000f, 11.550000f, 11.140000f, 1011280 },
{ 1253720137, 11.330000f, 11.090000f, 11.200000f, 11.330000f, 845737 },
{ 1253806537, 11.360000f, 11.120000f, 11.130000f, 11.350000f, 661239 },
{ 1253892937, 11.400000f, 11.190000f, 11.300000f, 11.350000f, 947261 },
{ 1254152137, 11.440000f, 11.250000f, 11.300000f, 11.440000f, 542043 },
{ 1254238537, 11.490000f, 11.300000f, 11.410000f, 11.400000f, 901120 },
{ 1254324937, 11.450000f, 11.250000f, 11.400000f, 11.300000f, 1591237 },
{ 1254411337, 11.500000f, 11.300000f, 11.440000f, 11.500000f, 845171 },
{ 1254497737, 11.450000f, 11.270000f, 11.300000f, 11.440000f, 846445 },
{ 1254756937, 11.580000f, 11.300000f, 11.440000f, 11.500000f, 2381803 },
{ 1254843337, 11.690000f, 11.450000f, 11.540000f, 11.650000f, 1397915 },
{ 1254929737, 11.840000f, 11.680000f, 11.700000f, 11.700000f, 1607400 },
{ 1255016137, 12.080000f, 11.800000f, 11.800000f, 11.960000f, 1783825 },
{ 1255102537, 12.320000f, 12.000000f, 12.070000f, 12.250000f, 1570618 },
{ 1255361737, 12.520000f, 12.350000f, 12.400000f, 12.440000f, 1255170 },
{ 1255448137, 12.580000f, 12.150000f, 12.550000f, 12.250000f, 1947450 },
{ 1255534537, 12.490000f, 12.240000f, 12.300000f, 12.360000f, 2256827 },
{ 1255620937, 12.470000f, 12.080000f, 12.450000f, 12.260000f, 2256836 },
{ 1255707337, 12.430000f, 12.100000f, 12.270000f, 12.290000f, 1989778 },
{ 1255966537, 12.650000f, 12.210000f, 12.350000f, 12.570000f, 2152491 },
{ 1256052937, 12.790000f, 12.590000f, 12.600000f, 12.710000f, 2677420 },
{ 1256139337, 12.780000f, 12.400000f, 12.780000f, 12.500000f, 1230832 },
{ 1256225737, 12.540000f, 12.200000f, 12.500000f, 12.500000f, 973076 },
{ 1256312137, 12.700000f, 12.460000f, 12.600000f, 12.600000f, 941507 },
{ 1256571337, 12.730000f, 12.460000f, 12.630000f, 12.580000f, 1013007 },
{ 1256657737, 12.600000f, 12.160000f, 12.520000f, 12.380000f, 1148034 },
{ 1256830537, 12.380000f, 11.660000f, 12.380000f, 11.970000f, 1975352 },
{ 1256916937, 12.080000f, 11.500000f, 11.970000f, 11.500000f, 1284680 },
{ 1257176137, 11.370000f, 11.150000f, 11.260000f, 11.300000f, 1196158 },
{ 1257262537, 11.220000f, 11.030000f, 11.160000f, 11.180000f, 1128519 },
{ 1257348937, 11.470000f, 11.160000f, 11.320000f, 11.400000f, 808150 },
{ 1257435337, 11.700000f, 11.150000f, 11.290000f, 11.700000f, 1296744 },
{ 1257521737, 11.850000f, 11.380000f, 11.800000f, 11.530000f, 575889 },
{ 1257780937, 11.700000f, 11.410000f, 11.670000f, 11.500000f, 436094 },
{ 1257867337, 11.680000f, 11.200000f, 11.590000f, 11.400000f, 931187 },
{ 1257953737, 11.500000f, 11.270000f, 11.490000f, 11.380000f, 649329 },
{ 1258040137, 11.300000f, 10.890000f, 11.180000f, 11.170000f, 1605771 },
{ 1258126537, 11.200000f, 11.000000f, 11.170000f, 11.170000f, 677617 },
{ 1258385737, 11.320000f, 10.740000f, 11.280000f, 10.960000f, 1241059 },
{ 1258472137, 11.010000f, 10.570000f, 10.980000f, 10.570000f, 2320938 },
{ 1258558537, 10.670000f, 10.430000f, 10.610000f, 10.600000f, 1636778 },
{ 1258644937, 10.780000f, 10.490000f, 10.600000f, 10.600000f, 973080 },
{ 1258731337, 10.650000f, 10.230000f, 10.600000f, 10.300000f, 869835 },
{ 1258990537, 10.500000f, 10.140000f, 10.380000f, 10.500000f, 754063 },
{ 1259076937, 10.660000f, 10.330000f, 10.400000f, 10.660000f, 1087273 },
{ 1259163337, 10.700000f, 10.440000f, 10.680000f, 10.620000f, 1787696 },
{ 1259249737, 10.570000f, 10.190000f, 10.430000f, 10.300000f, 2256943 },
{ 1259336137, 10.810000f, 10.090000f, 10.100000f, 10.700000f, 2943397 },
{ 1259595337, 10.700000f, 10.290000f, 10.700000f, 10.290000f, 1122333 },
{ 1259681737, 10.640000f, 10.420000f, 10.420000f, 10.550000f, 703170 },
{ 1259768137, 10.560000f, 10.450000f, 10.560000f, 10.540000f, 731467 },
{ 1259854537, 10.680000f, 10.450000f, 10.540000f, 10.600000f, 1080564 },
{ 1259940937, 10.560000f, 10.310000f, 10.420000f, 10.490000f, 1097465 },
{ 1260200137, 10.420000f, 10.280000f, 10.280000f, 10.390000f, 687170 },
{ 1260286537, 10.370000f, 9.880000f, 10.350000f, 10.010000f, 1754004 },
{ 1260372937, 10.140000f, 9.750000f, 9.950000f, 10.140000f, 2391228 },
{ 1260459337, 10.300000f, 10.020000f, 10.200000f, 10.300000f, 1097653 },
{ 1260545737, 10.520000f, 10.130000f, 10.390000f, 10.300000f, 1145867 },
{ 1260804937, 10.430000f, 10.000000f, 10.390000f, 10.300000f, 760342 },
{ 1260891337, 10.260000f, 10.100000f, 10.110000f, 10.100000f, 798194 },
{ 1260977737, 10.480000f, 10.000000f, 10.000000f, 10.480000f, 859980 },
{ 1261064137, 10.580000f, 10.200000f, 10.400000f, 10.580000f, 1129447 },
{ 1261150537, 10.580000f, 10.240000f, 10.580000f, 10.360000f, 1141131 },
{ 1261409737, 10.400000f, 10.200000f, 10.400000f, 10.340000f, 348655 },
{ 1261496137, 10.400000f, 10.180000f, 10.250000f, 10.360000f, 761681 },
{ 1261582537, 10.440000f, 10.250000f, 10.440000f, 10.380000f, 365296 },
{ 1262014537, 10.480000f, 10.210000f, 10.440000f, 10.310000f, 184127 },
{ 1262100937, 10.400000f, 10.220000f, 10.370000f, 10.370000f, 268928 },
{ 1262187337, 10.400000f, 10.240000f, 10.240000f, 10.260000f, 374614 },
{ 1262273737, 10.380000f, 10.250000f, 10.330000f, 10.290000f, 191767 },
{ 1262619337, 10.330000f, 10.130000f, 10.330000f, 10.200000f, 629324 },
{ 1262705737, 10.380000f, 10.190000f, 10.200000f, 10.380000f, 1279596 },
{ 1262878537, 10.500000f, 10.300000f, 10.380000f, 10.450000f, 884123 },
{ 1262964937, 10.670000f, 10.430000f, 10.500000f, 10.670000f, 1396721 },
{ 1263224137, 10.750000f, 10.520000f, 10.700000f, 10.640000f, 690814 },
{ 1263310537, 10.590000f, 10.150000f, 10.510000f, 10.150000f, 1069151 },
{ 1263396937, 10.750000f, 10.160000f, 10.200000f, 10.750000f, 2665842 },
{ 1263483337, 10.800000f, 10.520000f, 10.670000f, 10.680000f, 857784 },
{ 1263569738, 10.650000f, 10.240000f, 10.580000f, 10.460000f, 988426 },
{ 1263828938, 10.400000f, 10.030000f, 10.310000f, 10.200000f, 890014 },
{ 1263915338, 10.180000f, 9.650000f, 10.180000f, 10.090000f, 1290906 },
{ 1264001738, 10.240000f, 9.870000f, 10.240000f, 10.170000f, 1308316 },
{ 1264088138, 10.380000f, 9.870000f, 10.170000f, 10.380000f, 1654886 },
{ 1264174538, 10.330000f, 10.050000f, 10.120000f, 10.200000f, 758178 },
{ 1264433738, 10.400000f, 10.070000f, 10.070000f, 10.350000f, 970677 },
{ 1264520138, 10.310000f, 10.150000f, 10.300000f, 10.200000f, 458274 },
{ 1264606538, 10.220000f, 9.820000f, 10.220000f, 9.880000f, 927990 },
{ 1264692938, 9.970000f, 9.660000f, 9.880000f, 9.900000f, 1474743 },
{ 1264779338, 10.060000f, 9.800000f, 9.990000f, 9.950000f, 1059438 },
{ 1265038538, 10.150000f, 9.900000f, 10.000000f, 9.980000f, 647869 },
{ 1265124938, 9.990000f, 9.830000f, 9.900000f, 9.990000f, 672320 },
{ 1265211338, 10.120000f, 9.700000f, 10.040000f, 9.700000f, 908345 },
{ 1265297738, 9.770000f, 9.400000f, 9.700000f, 9.400000f, 985782 },
{ 1265384138, 9.320000f, 9.140000f, 9.160000f, 9.300000f, 1241836 },
{ 1265643338, 9.400000f, 8.960000f, 9.370000f, 9.000000f, 1935891 },
{ 1265729738, 9.300000f, 8.930000f, 9.000000f, 9.300000f, 1769364 },
{ 1265816138, 9.570000f, 9.180000f, 9.530000f, 9.270000f, 1383047 },
{ 1265902538, 9.470000f, 8.980000f, 9.420000f, 9.010000f, 1009741 },
{ 1265988938, 9.200000f, 8.630000f, 9.200000f, 8.630000f, 1171755 },
{ 1266334538, 8.900000f, 8.460000f, 8.900000f, 8.620000f, 2047635 },
{ 1266420938, 8.960000f, 8.750000f, 8.780000f, 8.900000f, 2201468 },
{ 1266507338, 9.320000f, 8.770000f, 8.900000f, 9.300000f, 2921043 },
{ 1266593738, 9.330000f, 9.170000f, 9.200000f, 9.330000f, 655521 },
{ 1266852938, 9.450000f, 9.170000f, 9.420000f, 9.300000f, 566245 },
{ 1266939338, 9.300000f, 8.760000f, 9.300000f, 8.760000f, 1378580 },
{ 1267025738, 9.030000f, 8.780000f, 8.950000f, 8.900000f, 849908 },
{ 1267112138, 8.900000f, 8.460000f, 8.900000f, 8.590000f, 1084970 },
{ 1267198538, 8.750000f, 8.530000f, 8.600000f, 8.560000f, 939906 },
{ 1267457738, 8.990000f, 8.810000f, 8.810000f, 8.860000f, 831447 },
{ 1267544138, 9.160000f, 8.870000f, 8.990000f, 9.150000f, 774443 },
{ 1267630538, 9.200000f, 8.900000f, 9.200000f, 9.010000f, 843576 },
{ 1267716938, 9.340000f, 9.060000f, 9.130000f, 9.310000f, 1669378 },
{ 1267803338, 9.380000f, 9.160000f, 9.370000f, 9.380000f, 731533 },
{ 1268062538, 9.430000f, 9.290000f, 9.410000f, 9.360000f, 570766 },
{ 1268148938, 9.400000f, 9.110000f, 9.400000f, 9.280000f, 659572 },
{ 1268235338, 9.540000f, 9.180000f, 9.200000f, 9.500000f, 1018660 },
{ 1268321738, 9.600000f, 9.300000f, 9.520000f, 9.430000f, 545311 },
{ 1268408138, 9.660000f, 9.320000f, 9.410000f, 9.600000f, 703401 },
{ 1268667338, 9.710000f, 9.450000f, 9.490000f, 9.450000f, 710162 },
{ 1268753738, 9.580000f, 9.140000f, 9.570000f, 9.400000f, 955355 },
{ 1268840138, 9.440000f, 9.040000f, 9.340000f, 9.040000f, 1147668 },
{ 1268926538, 9.180000f, 8.840000f, 9.170000f, 9.030000f, 1134056 },
{ 1269012938, 9.090000f, 8.890000f, 9.080000f, 9.070000f, 1276632 },
{ 1269272138, 9.200000f, 8.960000f, 9.050000f, 9.200000f, 1267601 },
{ 1269358538, 9.310000f, 9.040000f, 9.200000f, 9.170000f, 561357 },
{ 1269444938, 9.240000f, 9.040000f, 9.240000f, 9.080000f, 612473 },
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παπι · 20 Σεπτεμβρίου 2014

Πιπες. 0 help απο τον google..

Anyway, εκαψα μερικα κυτταρα και εβγαλα αυτο

#include <iostream>
#include <vector>
#include <algorithm>
#include <list>
#include <numeric>
typedef struct
{
	time_t		time;
	float		high;
	float		low;
	float		open;
	float		close;
	size_t		volume;
}DataPoint;


DataPoint data[] = 
{
#include "data.array"
};




void DetectPeaksAndDips(
	const std::vector<float>& input, 
	std::vector<size_t>& peaks,
	std::vector<size_t>& dips,
	size_t sample)
{
	size_t count = input.size() - sample;
	for(size_t i = sample; i < count; i++)
	{
		auto r = std::minmax_element(input.begin() + (i - sample), input.begin() + ( i + sample) );
		if( *r.second <= input[i] )
			peaks.push_back(i);
		if( *r.first >= input[i] )
			dips.push_back(i);
	}
	
}



void CreateSMA(const std::vector<float>& input, std::vector<float>& output, size_t val)
{
	size_t count = input.size() - val;
	output.resize(val,0.0f);
	for(size_t i = 0; i < count; i++)
	{
		float sum = std::accumulate(input.begin() + i,input.begin() + i + val,0.0f);
		output.push_back(sum/val);
	}
}



int main()
{
	std::vector<float> prices;
	//get close values
	for(size_t i = 0; i < _countof(data); i++)
		prices.push_back(data[i].close);

	std::vector<size_t> peaks,dips;

	//detect peaks and dips
	DetectPeaksAndDips(prices,peaks,dips,2);
	std::reverse(peaks.begin(), peaks.end());
	std::reverse(dips.begin(), dips.end());
	
	for(size_t i = 0; i < prices.size(); i++)
	{
		std::cout<<prices[i];
		//<<"\t"<<sma[i];
		if( !peaks.empty() && peaks.back() == i)
		{
			std::cout<<"\tPeek";
			peaks.pop_back();
		}
		if( !dips.empty() && dips.back() == i)
		{
			std::cout<<"\tDip";
			dips.pop_back();
		}
		std::cout<<std::endl;
	}
	
	return 0;
}

Το οποιο, ω ναι, δουλευει.

albNik · 20 Σεπτεμβρίου 2014

Ολα αυτα γίνονται στο πεδίο της συχνότητας (οχι του χρόνου).Ειναι μαθηματα που γινονται σε σχολες ηλεκτρολόγων, τηλεπικοινωνιων κλπ μετα το 2-3 εξάμηνο. Οι λέξεις κλειδι για google ειναι Fourier Transform, FFT ...

timonkaipumpa3 · 21 Σεπτεμβρίου 2014

Ολα αυτα γίνονται στο πεδίο της συχνότητας (οχι του χρόνου).Ειναι μαθηματα που γινονται σε σχολες ηλεκτρολόγων, τηλεπικοινωνιων κλπ μετα το 2-3 εξάμηνο. Οι λέξεις κλειδι για google ειναι Fourier Transform, FFT ...

Τσου.

Δεν είναι οι λέξεις κλειδί FFT... (και αλήθεια ποια άλλη λέξη κλειδί εννοείς γιατί μόνο μία έβαλες) και ΔΕΝ "γίνονται" [sic] (αλήθεια, ποια είναι αυτά που "γίνονται"; σε ποια αναφέρεσαι και έχεις πληθυντικό; ) στο πεδίο της συχνότητας.

Αφού δεν το γνωρίζεις, γιατί παραπλανάς;

albNik · 21 Σεπτεμβρίου 2014

O Site: Fourier Transform ειναι μαθηματικος μετασχηματισμος που μετατρεπει μια συνάρτηση απο το πεδιο του χρόνου σε αυτο της συχνοτητας.

Ο Site: FFT (Fast Fourier Fransform) ειναι αλγόριθμος για (γρήγορo) υπολογισμό του Site: Discrete Fourier Transform

'Γινονται' αναφερομουν στους αλγοριθμους για μετοχες (στο πρωτο μήνυμα το παπι).

https://www.google.gr/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=fourier+transform+stock+market+analysis

timonkaipumpa3 · 21 Σεπτεμβρίου 2014

O Site: Fourier Transform ειναι μαθηματικος μετασχηματισμος που μετατρεπει μια συνάρτηση απο το πεδιο του χρόνου σε αυτο της συχνοτητας.

Ο Site: FFT (Fast Fourier Fransform) ειναι αλγόριθμος για (γρήγορo) υπολογισμό του Site: Discrete Fourier Transform

'Γινονται' αναφερομουν στους αλγοριθμους για μετοχες (στο πρωτο μήνυμα το παπι).

https://www.google.gr/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=fourier+transform+stock+market+analysis

Αυτό και σταματάω εδώ γιατί θα πάμε πολύ offtopic...

Πολύ ωραία αυτά που λες και εντελώς entry level (τόσο entry που το butterfly είναι εργασία προπτυχιακού επιπέδου) ...

Τα ερωτήματά μου όμως παραμένουν.

α) Ποιες είναι οι άλλες λέξεις κλειδιά, εφόσον (όπως και τα δικά σου γραπτά δείχνουν) μόνο μία λέξη κλειδί αναφέρεις: FFT

β) Αφού δεν γνωρίζεις πώς υλοποιείται το peak picking και, προφανώς, ούτε από ανάλυση σημάτων... γιατί παραπλανάς τον κόσμο;

Εντελώς φιλικά, σου δίνω και ένα tip πως δεν γίνονται όλες οι αναλύσεις στο frequency domain...

Τσου.

Δεν είναι οι λέξεις κλειδί FFT... (και αλήθεια ποια άλλη λέξη κλειδί εννοείς γιατί μόνο μία έβαλες) και ΔΕΝ "γίνονται" [sic] (αλήθεια, ποια είναι αυτά που "γίνονται"; σε ποια αναφέρεσαι και έχεις πληθυντικό; ) στο πεδίο της συχνότητας.

Αφού δεν το γνωρίζεις, γιατί παραπλανάς;

Υ.Γ. Και επειδή με έπιασες στα καλά μου... σου δίνω και ένα ακόμα εντελώς φιλικό tip. Δεν είναι μόνο τα time και freq domains.

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