Series: Time-series – QUANT dira-t-on?

02 - Statistical Moments

Introducing the first 4 moments of statistical analysis: mean, standard deviation, skewness and kurtosis. Showing how to use R and Python on these concepts. We then provide 2 methods to transform data in order to bring it closer to a normal distribution.

03 - Autocorrelation, Stationarity and Random-Walk - Part 1

A dive into the concepts of autocorrelation and stationarity of time series. We also get into how to plot correlogram using R and Python, random-walk, white-noise.

04 - Time series decomposition

Introducing time series decomposition. We first show how to compose time-series using linear trend, seasonality and then white noise.

05 - AR, MA and ARIMA models

Introducing Arima - Autoregressive Integrated Moving Average.