Time Series Analysis

Today, many companies have adopted time series analysis and forecasting methods to develop their business strategies. These techniques help in evaluating, monitoring, and predicting business trends and metrics. Time series analysis is beneficial and is commonly used for economic forecasting, yield projection, inventory studies, census analysis, sales forecasting, stock market analysis, and budgetary analysis.

What Is a Time Series?
Time series is an ordered sequence of data points spread over a period of time. Here, time is generally an independent variable while the other variable/s keep changing values. The time series data is monitored over constant temporal intervals. This data can be in any measurable and quantifiable parameter related to the field of business, science, finance, etc.

What is Time Series Analysis?
Time series analysis refers to identifying the common patterns displayed by the data over a period of time. For this, experts employ specific methods to study the data’s characteristics characteristics and extract meaningful statistics that eventually aid in business forecasting.

Learn Forecasting our Data science course designed for beginners for a better understanding of the concept.

Time Series Analysis and Forecasting Tactics
Certain features of the given time series aid in creating are used to create models that help predict assist in predicting business metrics and the future behaviour of business metrics. The better you can figure out the characteristics of the given data’s characteristics, the more accurate the forecasts will be. Below is an overview of 18 crucial concepts, methods, and things to know for efficient business forecasting:

  1. Time series forecasting methods are a group of statistical techniques that can be vital for estimating different variables and be used for any business for estimating different variables.
  2. To obtain accurate forecasts, you need to check for three essential features in a time series. These are autocorrelation, seasonality, and stationarity.

Autocorrelation and Seasonality

  1. Autocorrelation is a mathematical term that indicates the extent of similarity between the given time series and its delayed version over a particular time. This time series refers to a set of values of a variable/entity.
  2. Autocorrelation helps determine the relationship between current values and the past values of an entity. By using the past and current data, the professionals can identify and analyse the data patterns, establish relations, and plan for the future.
  3. When an entity exhibits similar values periodically, i.e. after every fixed time interval, it makes way for measuring seasonality. For example, business sales of certain products show a similar increase in every festive season.
  4. Seasonality lays the ground for predictability of the variable as per a particular time of the day, month, season, or occasion. With the help of seasonal variation data, the salespeople can devise their strategy ahead of that specific period.

Stationarity and Trends

  1. When the statistical properties of a time series’ statistical properties remain constant over time, it is said to be stationary. In other words, the mean and variance of the series stay the same. Entities like stock prices are usually not static.
  2. Stationarity of a time series is checked by conducting a KPSS test, Dickey-Fuller test, or extended versions of these tests. Methods to detect stationarity are primarily statistical in nature. These tests basically evaluate a null hypothesis in one way or the other.
  3. Stationarity is regarded as quite crucial in a series, else a model displaying the data shows different accuracy at different time points. So, before modelling, the professionals use some techniques to transform a given non-stationary time series into a stationary one.
  4. Trends are recorded over a long time. Depending upon the nature of the entity and related influencing factors, its trend may decrease, increase, or remain stable. For example, population, birth rate, death rate, etc. are some of the entities that mostly show movement and thus, cannot form a stationary time series.

Resource Article : https://www.excelr.com/blog/data-science/forecasting/18-time-series-analysis-tactics-that-will-help-you-win-in-2020

Add a Comment

Your email address will not be published. Required fields are marked *