  Forecasting the number of trips in bike-sharing systems and its volatility
over time is crucial for planning and optimizing such systems. This paper
develops timeseries models to forecast hourly count timeseries data, and
estimate its volatility. Such models need to take into account the complex
patterns over various temporal scales including hourly, daily, weekly and
annual as well as the temporal correlation. To capture this complex structure,
a large number of parameters are needed. Here a structural model selection
approach is utilized to choose the parameters. This method explores the
parameter space for a group of covariates at each step. These groups of
covariate are constructed to represent a particular structure in the model. The
statistical models utilized are extensions of Generalized Linear Models to
timeseries data. One challenge in using such models is the explosive behavior
of the simulated values. To address this issue, we develop a technique which
relies on damping the simulated value, if it falls outside of an admissible
interval. The admissible interval is defined using measures of variability of
the left and right tails. A new definition of outliers is proposed based on
these variability measures. This new definition is shown to be useful in the
context of asymmetric distributions.
