1. A computer-implemented method for analyzing performance in an online professional network, the method comprising: receiving time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval; receiving time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval; and performing a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics; wherein performing the time series analysis comprises, using the received time series data for user actions and performance metrics to construct a time series model, which comprises a regular time series model, and also a seasonal time series model to handle seasonal patterns in the time series data, wherein the seasonal time series model includes a seasonality parameter s, which is determined by performing an autocorrelation function (ACF) on the performance metrics, fitting the time series model by, stationarizing the time series model based on differences between data points in the time series data, constructing and solving a structural vector autoregression function for the time series model to produce a first set of residuals, constructing and solving a structural vector moving average function for the time series model using the first set of residuals to produce a second set of residuals, and selecting lagged exogenous variables for the time series model based on the second set of residuals, and solving the time series model using a multivariate regression technique.