This paper mentions a mechanism to clean high frequency data of outliers. The setting is NYSE TAQ(Trades and Quotes data) and many initial filters(data cleaning) applied are specific to NYSE. However the mechanism for removing outliers that is mentioned by is market agnostic. The key idea behind the method is to choose k neighbor prices + a fudge factor gamma, and compute a trimmed mean and standard deviation of the k neighboring prices.

highfrequency is an R package that can be used to 1) clean and aggregate high frequency data, 2) compute realized volatility measures 3) compute liquidity measures. The package is an improved version of two other R packages, RTAQ and realized. The vignette for thepackage explains two models, HAR and HEAVY models. HAR models rely on jump modeling and one needs to have a decent idea of Levy processes to appreciate the HAR variants.

The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modeled separately. These models for the components are put together to form a single model called a state space model which provides the basis for analysis. The book is primarily aimed at applied statisticians and econometricians. Not much of math background is needed to go through the book,at least the first part of the book.

** Your home is whatever your love more than yourself.** - Elizabeth Gilbert [youtube https://www.youtube.com/watch?v=_waBFUg_oT8?rel=0]

Via TP - Data Scientist Interviews Parham Aarabi, Founder of Modi Face ModiFace technology simulates skin-care and cosmetics products on user photos. So, a skin care product that reduces dark spots, or a shiny lipstick, or a glittery eyeshadow … we specialize in making custom simulation effects for all facial products. This is us as a core Pick problems that in your view truly matter.