Time series is a sequence of observations recorded at regular time intervals. This guide walks you through the process of analyzing the characteristics of a given time series in python. Time Series Analysis in the IPython File.
Contents
- What is a Time Series?
- How to import Time Series in Python?
- What is panel data?
- Visualizing a Time Series
- Patterns in a Time Series
- Additive and multiplicative Time Series
- How to decompose a Time Series into its components?
- Stationary and non-stationary Time Series
- How to make a Time Series stationary?
- How to test for stationarity?
- What is the difference between white noise and a stationary series?
- How to detrend a Time Series?
- How to deseasonalize a Time Series?
- How to test for seasonality of a Time Series?
- How to treat missing values in a Time Series?
- What is autocorrelation and partial autocorrelation functions?
- How to compute partial autocorrelation function?
- Lag Plots
- How to estimate the forecastability of a Time Series?
- Why and How to smoothen a Time Series?
- How to use Granger Causality test to know if one Time Series is helpful in forecasting another?
- What Next