Financial Time Series Analysis using Wavelets

1. QX Data Science Event | 10.05.2019 | QX Manor in Frankfurt am Main

Description:

Presentation by Markus Vogl at the 1. QX Data Science Event about Financial Time Series Analysis using Wavelets.
Contains Explanations of Financial Time Series Properties (e.g. Markov, Martingale, Stationarity & Gaussianity versus Fractality & Momentum), Signal Theory (e.g. Fourier Analysis, Short Time Fourier Analysis & Continuous as well as Discrete Wavelet Transformations).
Concludes with outlook into research on Wavelet Neural Networks, Fractals & Chaos Theory.

Partners, Event-Team & Presentor:

#lecture #university #timeseriesanalysis #timeseries #statistics #python #pythonprogramming #pythontutorial #data #datascientist #finance #stockmarket #quantitativefinance #marketanalysis #wavelet #signal #signalanalysis #fourier_series #fouriertransform #wavelengths #waveletneuralnetwork #WNN #wnn

[guliyevshahriyar]

seeing ``time-series", ``fourier analysis", ``wavelets", ``w-neural networks'' = i said, ooh lovely 🤓🤓🙌🙌

[eladwarshawsky]

So in financial prediction it's important to only calculate the wavelet transform locally (in segments of "x" length), so as to make sure that the shifting/scaling does not create data leakage?

[HKHasty]

As I lover of physics, I especially liked the Heisenberg principle reference.

[hurstcycles]

Excellent analysis Markus. One question on the interpretation of the time frequency analysis of log returns. The localisation in time is sharp but the frequency information diffuse. Is it a case of making the assumption that if *any* significant frequency information is obtained at any timepoint that can be interpreted as indicative of predictability? Rather than trying to tune the uncertainty principle by adjusting wavelet parameters to focus in on specific frequencies while accepting some smearing in time?

[machinic-eh]

Awesome work. Do you have a website containing links and references to your articles and thesis?

[FreeMarketSwine]

Is it possible to decompose the signal and train a separate model on each component and then recombine the predictions of the component models?

[franzmoser]

Not bad! On the last slide you mixed up HP- and LP-Filter with Approx. and Detail coefficients. Details are connected with HP and Approximations with LP

[lukaszmajkowski]

very nice tut, how are your results ?:)

[nirajpaija]

How can we estimate fourier analysis in R?

[MlNECRAFT]

oh so i can just max decompose CWT any power of 2 set of return data, break that down into power, phase, or even just the coefficients as those alone are time/scale dependent, then just feed the 2 3d arrays of data into a combination of cnn and lstm custom architecture . i could feed that with the original signal for cross validation or whatever it called so the model can tie in frequency understanding, with M.I.N.N. kinda vibes or whatever and then like predict the future but like with the nice colors of your heatmap. its like extending the heatmap into time, shifting the focus of the CWT, performing a discretized iCWT with respect to the original order of decomposition so our resolution is perfecto r something nd yeah perfect

[mentalistize]

The intresting thing would be creating a strategy based on non linear stochastic wavelet like a mathematician on arxiv made.

[WahranRai]

Why not choosing synthetic voice for your presentation ?