Kolmogorov Space in Time Series Data

TL;DR

This paper proves that the space of time series data forms a Kolmogorov space with a T0-separation axiom, introducing a new extradimension and revealing hidden dimensions through a novel theoretical framework.

Contribution

It introduces a new theoretical framework for analyzing time series data as a Kolmogorov space with hidden dimensions, utilizing loop space and spinor fields.

Findings

Time series space is a Kolmogorov space with T0-separation.

Existence of eight hidden dimensions in the space.

Algorithmic approach based on empirical mode decomposition.

Abstract

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for time series data. Our concept is realized as the algorithm of empirical mode decomposition and intrinsic time scale decomposition and it is subsequently used for preliminary analysis on the real time series data.