Modeling Time Series Data - Recap

Key Takeaways

The key takeaways from this section include:

A white noise model has a fixed and constant mean and variance, and no correlation over time
A random walk model has no specified mean or variance, but has a strong dependence over time
The Pandas .corr() method can be used to return the correlation between various time series in the DataFrame
Autocorrelation allows us to identify how strongly each time series observation is related to previous observations
The Autocorrelation Function (ACF) is a function that represents autocorrelation of a time series as a function of the time lag
The Partial Autocorrelation Function (or PACF) gives the partial correlation of a time series with its own lagged values, controlling for the values of the time series at all shorter lags
ARMA (Autoregressive and Moving Average) modeling is a tool for forecasting time series values by regressing the variable on its own lagged (past) values
ARMA models assume that you've already detrended your data and that there is no seasonality