A Statistical Toolkit for Preliminary Time-Series Analysis
Summary
The article presents a general-purpose tool for preliminary analysis of numeric sequences, including price series. It reports descriptive statistics such as mean, variance, skewness, and kurtosis, alongside normality tests, outlier limits, histograms, autocorrelation and partial autocorrelation plots, and spectral estimates. The method uses calculated moments for distribution summaries and Jarque–Bera tests, while spectral analysis is based on autocorrelation. Results can be displayed through a replaceable output method, with the example generating charts in a browser.
The tool is intended to screen a sequence and guide further investigation, not to establish a trading edge. The article notes that inputs must have at least eight observations and non-negligible variance; practical maximum length depends on available computing resources. Jarque–Bera p-values can be inaccurate for short samples, and spectral estimates remove the sequence mean, which may distort low-frequency behavior in some cases. Outlier limits are descriptive only and do not modify the data.
Key ideas
- The toolkit combines descriptive statistics, distribution checks, outlier limits, and time-series plots for initial screening.
- Jarque–Bera and adjusted Jarque–Bera tests assess departures from normality, but short samples limit p-value accuracy.
- Autocorrelation, partial autocorrelation, and spectral estimates can help investigate dependence and periodic structure.
- The method requires enough observations and non-negligible variance for stable calculations.
- Because spectral estimates use autocorrelation, they operate on a mean-adjusted sequence and can distort low-frequency behavior.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.