Data Over Time: Time Series Analysis
Time series analysis has become an essential tool in many industries for understanding patterns and trends in data over time.
This blog will delve into the world of time series analysis, exploring its definition, techniques, and applications. So let's take a closer look at time series analysis.
Contents :
- Forecasting
- Time Series Meaning
- Assumption
Forecasting :
''Forecasting is the process of making predictions about future events based on past and current data. ''
In time series analysis, forecasting refers to the prediction of future values of a time-based variable based on its past behavior. This process involves building a model to capture the patterns and relationships in the data and using that model to make predictions about future values. Forecasting can be used in various fields such as finance, economics, weather, and sales, to make informed decisions and plan for future outcomes.
Time Series Meaning :
Assumption :
Here are some common assumptions in time series analysis:
Stationarity: The assumption that the statistical properties of a time series, such as mean and variance, are constant over time.
Independence: The assumption that the observations in a time series are independent of each other, meaning that each observation is not influenced by previous observations.
Linearity: The assumption that the relationship between the independent and dependent variables in a time series is linear.
Normality: The assumption that the errors in a time series model are normally distributed, which simplifies the calculation of statistical tests and confidence intervals.
Ergodicity: The assumption that the time series can be treated as a stationary process, even if it is not actually stationary in practice.
It is important to understand these assumptions and to evaluate their validity in a particular time series analysis, as violating these assumptions can lead to incorrect results and predictions.
