Time Series
TIME SERIES ANALYSIS
Introduction
Economic and business time series analysis is a major field of research and application. This analysis method has been used for economic forecasting, sales forecasting, stock market analysis and company internal control. In this paper, we will talk about time series and review techniques that are useful for analyzing time series data.
Definition of Time Series and Time Series Analysis
Time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series data often arise when monitoring industrial processes or tracking corporate business metrics.
The analysis of time series is based on two basic assumptions. One is successive values in the data file represent consecutive measurements taken at equally spaced time intervals. The other assumption is that time is the only one independent variable in time series function.
Applications of Time Series Models
• Identify the nature of the phenomenon represented by the sequence of observations, and
• Fit a model and proceed to forecasting, monitoring or even feedback and feedforward control.
Both of these goals require that the pattern of observed time series data is identified and described. Once the pattern is established, we can interpret and integrate it with other data and use the identified pattern to predict future events.
Decomposition Analysis of Time Series
In order to identify patterns that appear simultaneously in a time series, we use decomposition analysis to separate and decompose different influences or components out of the ‘raw’ data,. In general, there are four types of components in time series analysis: trend, seasonal variation, cyclical variation and irregular variation. Thus, the value of the time series Y can be represented by the following equations: Yt = Tt * St* Ct *It or Yt = Tt +St +Ct +It
Where Yt = Actual value of the data in the time series at time t Tt = Trend value at time
Introduction
Economic and business time series analysis is a major field of research and application. This analysis method has been used for economic forecasting, sales forecasting, stock market analysis and company internal control. In this paper, we will talk about time series and review techniques that are useful for analyzing time series data.
Definition of Time Series and Time Series Analysis
Time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series data often arise when monitoring industrial processes or tracking corporate business metrics.
The analysis of time series is based on two basic assumptions. One is successive values in the data file represent consecutive measurements taken at equally spaced time intervals. The other assumption is that time is the only one independent variable in time series function.
Applications of Time Series Models
• Identify the nature of the phenomenon represented by the sequence of observations, and
• Fit a model and proceed to forecasting, monitoring or even feedback and feedforward control.
Both of these goals require that the pattern of observed time series data is identified and described. Once the pattern is established, we can interpret and integrate it with other data and use the identified pattern to predict future events.
Decomposition Analysis of Time Series
In order to identify patterns that appear simultaneously in a time series, we use decomposition analysis to separate and decompose different influences or components out of the ‘raw’ data,. In general, there are four types of components in time series analysis: trend, seasonal variation, cyclical variation and irregular variation. Thus, the value of the time series Y can be represented by the following equations: Yt = Tt * St* Ct *It or Yt = Tt +St +Ct +It
Where Yt = Actual value of the data in the time series at time t Tt = Trend value at time