Time series is a very common datatype that we usually encounter in our daily life. Home energy usage, Financial prices, weather, and even weight are all examples of data that can be collected at regular intervals. In daily work , data scientist might encounter time series and learning how to model them is an important skill in the data science toolbox.

The Time series is the sequence of observations recorded at regular time intervals. Depending on the frequency of observations, a time series may typically be hourly, daily, weekly, monthly, quarterly and annual. At times, you might have seconds and minute-wise time series as well, like, number of clicks and user visits every minute etc. It is the preparatory step before you develop a forecast of the series. Besides, time series forecasting has enormous commercial significance because the stuff that is important to a business like demand and sales, number of visitors to a website, stock price etc are essentially time series data.

Time series analysis involves understanding various aspects about the inherent nature of the series so that you are better informed to create meaningful and accurate forecasts. At first, financial time series such as daily stock market prices were developed, but the robust and flexible data structures in pandas can be applied to time series data in many domains, including public health, business, science, engineering, and many others.

With python tools you can easily organize, transform, analyze, and visualize your data at any level of granularity – zooming out to explore variations on different time scales and examining the details during specific time periods of interest,, such as monthly or annual aggregations, recurring patterns, and long-term trends. In short, a time series is any data set where the values are measured at different points in time. Usually, time series are uniformly spaced at a specific frequency, like for example, hourly weather measurements, daily counts of website visits, or monthly sales totals. Python has Pandas time series tools that can be used to apply equally well to either type of time series.

Author: STEPS