Background

Time series data consist of any data collected over time. Examples of such data include: climate data, financial data such as stock prices, streamflow data, etc. Analysis of time series data is significant in decision making , management and allocation of resources such as predicting current and future water demands, business sales etc. Generally, time series analysis involves understanding and discerning of characteristics inherent to the time series data under analysis, which is significant in developing robust post-processing methodologies for time series forecasting and modeling, trend and pattern detection etc. These series of tutorials will walk you through different steps in analysis of the characteristics of any given time series data using Python. It should be noted these similar steps can be applied in other programming languages such as R.