Time series: this refers to the sequence of data points that occur in successive order over a period of time. A time series in investing records the movement of selected data points, such as the price of a security, over a set period of time, with data points recorded at regular intervals. There is no minimum or maximum time requirement, allowing the data to be obtained in a fashion that gives the information desired by the investor or analyst reviewing the activity.
The main importance of the time series is for predictive analysis.
Time series analysis can also be defined as a method of analyzing a collection of data points over a period of time. Instead of recording data points intermittently or randomly, time series analysts record data points at consistent intervals over a defined period of time.
While time-series data is information gathered over time, various types of information describe how and when that information was gathered. For example:
Time series data is a collection of observations on the values that a variable takes at various points in time.
Cross-sectional data: data from one or more variables that were collected simultaneously.
Pooled data: It is a combination of cross-sectional and time-series data.
The importance of time series analysis
Time series analysis has a wide range of importance in different fields, such as sales, economics, and many more. However, the common point is the technique used to model the data over a period of time. The reasons for time series analysis are as follows:
- Features:Time series analysis can be used to track features like trend, seasonality, and variability.
- Prediction: time series analysis can be used for prediction purposes by studying the patterns of the data available. -Inferences: You can predict the value and draw inferences from the data using time series analysis.
Time Series Analysis Types
- Classification: It identifies categories in the data.
- Curve-fitting: it plots the data on the curve to identify the relationship between variables in the data.
- Descriptive analysis: patterns in the time-series data, such as trends, cycles, and seasonal variations, are identified
- Explanative analysis: It attempts to comprehend the data and the relationships between it and cause and effect.
- Segmentation: It splits the data into segments to reveal the source data's underlying properties.
*Time series analysis example *
Non-stationary data is defined as data that fluctuates over time or is affected by time and is evaluated using time series analysis.Because currency and sales fluctuate, businesses such as finance, retail, and e-commerce regularly employ time series analysis. Stock market analysis is a great illustration of time series analysis in action, especially when combined with automated trading algorithms.
Time series analysis can be used in:
Rainfall measurements
Automated stock trading
Industry forecast
Temperature readings
Sales forecasting
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