Introduction
Time series analysis is used across many fields of study, including finance, health, social sciences, business, and the physical sciences. It is a specialized branch of statistics that aids in understanding sequential data points ordered in time. Technically, time series analysis is the process of analyzing data that is collected over a specific period to determine the long-term trend and forecast the future.
Understanding Time Series
A time series is a sequence of observations taken sequentially in time. These observations can be taken at regular intervals (such as daily, weekly, or annually), or irregular intervals (such as the occurrence of specific events). Time series analysis makes use of the time series data to extract meaningful statistics and data characteristics. It’s widely used for non-stationary data, which has continuously varying mean.
Significance of Time Series Analysis
Time series analysis helps in analyzing the past, which comes in handy to forecast future events. For instance, a company with a year’s sales data could foresee the sales for next year. Purpose of time series analysis includes understanding underlying forces and structure that produced the observed data, fitting a model and proceeding to forecasting, monitoring or feedback and control.
Rudiments of Time Series Analysis
Trend
A trend exists when there is a long-term increase or decrease in the data. It does not have to be linear. Sometimes we will refer to a trend “changing direction” when it might go from an increasing trend to a decreasing trend.
Seasonality
A seasonal pattern occurs when a time series is influenced by seasonal factors such as the time of the year or the day of the week. Seasonality can be seen as the repetitive and predictable movement around the trend.
Cyclic
A cyclic pattern exists when data exhibit rises and falls that are not of fixed frequency. These fluctuations are usually due to economic conditions, and are often related to the “business cycle”. The duration of these fluctuations is usually at least 2 years.
Methods of Time Series Analysis
The four main methods used in time series analysis include, Auto Regression (AR), Moving Average (MA), Auto Regressive Moving Average (ARMA), and Auto Regressive Integrated Moving Average (ARIMA). Additionally, various other algorithms like Long Short Term Memory (LSTM), Prophet and Vector Auto Regression (VAR) are also utilized depending on the nature of the problem and the type of data.
Conclusion
Time series analysis reveals patterns and behaviors within the collected data that may otherwise be challenging to discern. It permits both visual interpretations and quantifiable changes to model and predict future points. It also allows us to account for noise or error in our data. Whatever field or application you’re dealing with, a strong understanding of time series analysis will pave the way towards a strong analytical mindset.
Frequently Asked Questions
- What is Time Series Analysis?
Time Series Analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals.
- Why is Time Series Analysis important?
Time series analysis can be used to forecast trends in economics, weather, and capacity planning, among other areas. It is a beneficial tool when you want to make future predictions from past data.
- What is a trend in Time Series Analysis?
A trend in Time Series Analysis is a steady, directional movement in data over time.
- What is Seasonality in Time Series Analysis?
Seasonality in Time Series Analysis refers to regular and predictable changes that recur every calendar year.
- What methods are used in Time Series Analysis?
Auto Regression (AR), Moving Average (MA), Auto Regressive Moving Average (ARMA), and Auto Regressive Integrated Moving Average (ARIMA) are some of the most common methods used in Time Series Analysis.
