Hi, This is the 1st and introductory submission for the DATA606 project. Best. -Koohyar

This week's data dive is on Time Series Modelling, a statistical concept that uses time as an explanatory variable in a model. It is a crucial aspect of analyzing and forecasting time-dependent data, which involves studying the patterns and characteristics of a sequence of observations collected over time, to understand the underlying data-generating process and make accurate predictions about future values. As discussed earlier in the week; the key components of time series modeling include: trend, seasonality, cyclical patterns, and random noise or irregular fluctuations. Time series modeling involves several steps, including data exploration and visualization, stationarity testing, model identification, parameter estimation, diagnostic checking, and forecasting. The choice of model depends on the characteristics of the data we are looking at, the presence of trend and seasonality, and the desired level of accuracy and interpretability.