Time Series is defined as a set of observations taken at a particular period of time. For example, having a set of login details at regular interval of time of each user can be categorized as a time series. On the other hand, when the data is collected at once or irregularly, it is not taken as a time series data. Time series is a sequence that is taken successively at the equally pace of time. It appears naturally in many application areas such as economics, science, environment, medicine, etc.

Understanding timely patterns/characteristics in data are becoming very critical aspect in analyzing and describing trends in business data . Example Use case 1: Fitness device market is built around buy people to help track fitness related data to monitor effectiveness of their fitness exercises. Example Use Case 2: Sales growth of a product over period of time is a good indicator of sales performance of a product manufacturing company. A typical time series model can exhibits different patterns. Therefor it is important to understand components of a time series in detail .

Time Series is defined as a set of observations taken at a particular period of time. For example, having a set of login details at regular interval of time of each user can be categorized as a time series. On the other hand, when the data is collected at once or irregularly, it is not taken as a time series data. Stock Series - It is a measure of attributes at a particular point in time and taken as a stock takes. Flow Series - It is a measure of activity at a specific interval of time. It contains effects related to the calendar. Time series is a sequence that is taken successively at the equally pace of time. It appears naturally in many application areas such as economics, science, environment, medicine, etc.

This article is not about smoothing ore into gems though your may find a few gems herein. Systematic Pattern and Random Noise In "Components of Time Series Data", I discussed the components of time series data. In time series analysis, we assume that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error), which often makes the pattern difficult to identify. Most time series analysis techniques involve some form of filtering out noise to make the pattern more noticeable. Two General Aspects of Time Series Patterns Though I have discussed other components of time series data, we can describe most time series patterns in terms of two basic classes of components: trend and seasonality.

Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics of the data. In this blog, we will begin our journey of learning time series forecasting using python. We will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. Time series analysis is the collection of data at specific intervals over a period of time, with the purpose of identifying trends, cycles, and seasonal variances to aid in the forecasting of a future event. Data is any observed outcome that's measurable. Unlike in statistical sampling, in time series analysis, data must be measured over time at consistent intervals to identify patterns that form trends, cycles, and seasonal variances.