Multifractality is a concept that extends locally the usual ideas of fractality in a system. Nevertheless, the multifractal approaches used lack a multifractal dimension tied to an entropy index like the Shannon index. This paper introduces a generalized Shannon index (GSI) and demonstrates its application in understanding system fluctuations. To this end, traditional multifractality approaches are explained. Then, using the temporal Theil scaling and the diffusive trajectory algorithm, the GSI and its partition function are defined. Next, the multifractal exponent of the GSI is derived from the partition function, establishing a connection between the temporal Theil scaling exponent and the generalized Hurst exponent. Finally, this relationship is verified in a fractional Brownian motion and applied to financial time series. In fact, this leads us to propose an approximation called local fractional Brownian motion approximation, where multifractal systems are viewed as a local superposition of distinct fractional Brownian motions with varying monofractal exponents. Also, we furnish an algorithm for identifying the optimal q-th moment of the probability distribution associated with an empirical time series to enhance the accuracy of generalized Hurst exponent estimation.

The data sets used in this research are divided into two parts:

1. Data from fractional Brownian motion (fBm) simulations: These data are generated using the Wood-Chan algorithm for fractional Gaussian noise. Additionally, to verify the value of the Hurst exponent of each fBm sample, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method implemented in Python by Gorjao et. al. (https://doi.org/10.1016/j.cpc.2021.108254) Is used.
2. Financial time series data: This data is generated by an API designed in Python to directly download financial time series data from Yahoo Finance with the ticker name. From these, the time series of returns, log returns (log-returns), absolute log returns and volatilities of log returns are generated.