The main benefit of this indicator is the ability to see multiple higher timeframes at ones to get a better overview of signals that could mark possible trend reversals with more weight than those on the selected timeframe. Since the higher timeframes are calculated automatically, the user needs to set a Period Multiplier that multiplies the selected timeframe...
Generic Markov Chain type functions.
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the
probability of each event depends only on the state attained in the previous event.
Understanding Markov Chains, Examples and Applications. Second Edition. Book by Nicolas...
The Viterbi Algorithm calculates the most likely sequence of hidden states *(called Viterbi path)*
that results in a sequence of observed events.
viterbi(observations, transitions, emissions, initial_distribution)
Calculate most probable path in a Markov model.
observations (int ) : array ....
Baum-Welch Algorithm, also known as Forward-Backward Algorithm, uses the well known EM algorithm
to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed
### Function List:
> `forward (array pi, matrix a, matrix b, array obs)`
> `forward (array pi, matrix a,...
"In time series analysis, dynamic time warping (DTW) is an algorithm for
measuring similarity between two temporal sequences, which may vary in
speed. For instance, similarities in walking could be detected using DTW,
even if one person was walking faster than the other, or if there were
accelerations and decelerations...
The script is a simple calculator to obtain numbers of Fibonacci, Tribonacci or Tetranacci Sequence.
The script contain calculations for constants (up to 16 digits) that could be used as one of the sequence's number.
The Calculator has 3 modes. Users can define the numbers to initialize the sequence in the options:
- The Fibonacci Sequence is the series of...
The previously proposed sequential filter aimed to filter variations lower than a certain period, this allowed to remove noisy variations and retain only the closing price values that occurred after a consecutive up/down, however because of the noisy nature of the closing price large filtering was impossible, in order to tackle to this problem the same indicator...
A moving average that weighted with Farey fractions. It matches a standard linear weighted average almost one-to-one. Why? Because both averages have strictly monotonic weighting sequences and assign a higher weight to latests data. So, Farey weights are just scaled to linear ones. Instead of specifing period you specify an order of Farey sequence. To learn more...