Интуитивное понимание и визуализация анализа временных рядов на основе Persistence Diagram и Barcode
Интуитивное понимание и визуализация анализа временных рядов на основе Persistence Diagram и Barcode.
Analysis of data transformation in machine learning, focusing on topological analysis for time series. Using local minimums and maximums to form bars on charts.Introduction to Topological Analysis in Data Science : Explaining the mathematical complexity of topological analysis in data science
Discussion about two transformations in topological analysis and their applications in graph and time series analysis
Using local minimum and maximum points for stock analysis : Identify the local minimum and maximum points to track stock trends
Use moving averages to mark significant points on a chart
The process from a local minimum to a local maximum ends here : Mark the origin of the local minimum
Set the end of the wave of movement according to local maximum, wait for the next wave
The chart shows the distribution of values relative to a neutral line. : There are multiple bars on the chart representing different periods.
The length of each bar represents the values that were recorded during the respective period.
Understanding the closer value to the axis : Designations are convenient, closer value to axis looks like noise
Farther value from axis looks like emissions, relevant in finance and market
A chart started from a minimum and reached maximum values : The chart may represent the movement of a stock or cryptocurrency
Analyzing real data from the chart's moving averages may help determine opportunities to invest
Capturing local minimums and maximums can help with future calculations : Waiting for local maxima can improve accuracy
Analyzing daily or weekly trends can help determine the presence of trends
An idea for future implementation based on video analysis : Further analysis of topology and histograms
Explanation at current understanding level, but necessary to move forward
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