Forecasting financial trends, seasonality, and sales could be a fascinating yet challenging task if one does not have access to proper tools and models. Time always plays an important factor in determining different trends over time, and one simply cannot miss this variable. Whether you are interested in looking for a financial market trend, sales pattern, electricity consumption, or market conditions, time is the only constant in every model. The time series model operates on the very principle of time and helps companies and businesses analyze patterns and forecast with the data provided. For example, companies can use a time series model to forecast peak consumption levels of electricity. This will, in return, guide them further on the price and production of electricity. There are many such examples of time series models and their application in real time.
What is a time series model?
A time series model is a set of points that helps to analyze patterns and forecast trends or the future using time as the independent variable. The goal of every organization is to predict the future and make decisions according to those predictions. Therefore, businesses use the time series model extensively to make forecasts and predictions for data-driven decision-making. It is also known as time series analysis. According to this method or model, several data points are collected for the purpose of analysis over a certain period of time. But one thing should be kept in mind that these data points are not randomly or intermittently collected. Rather, the time series model focuses on consistent data collection over uniform intervals at specific time periods to forecast with utmost accuracy and deliberation. The various data points specify the nature of the information and how and when it was gathered. Following are the different types of data points for time series analysis.
- Time series data: It refers to the collection of information or values that the variable assumes at different time intervals.
- Cross-sectional data: It is the collection of data points from different variables collected simultaneously.
- Pooled data: It is the collective result of cross-sectional and time series data points.
What are the different aspects/characteristics of the time series model?
There are several factors and aspects that impact the time series model, including:
• Stationarity
• Seasonality
• Autocorrelation
We often associate these characteristics with data and use these to extract possible forecasts.
1. Stationarity
It is one of the pivotal and fundamental characteristics of time series. A time series becomes or is called stationary when the statistical properties remain the same over time and do not undergo any prominent change. In simple words, the mean, variance, and covariance remain stagnant and unchanged by time variation or intervals. These factors become independent of time. For instance, stock prices are, at times, not stationary because we may witness a growing trend with time. They are non-stationary because of high volatility over time because of changing variance. In ideal situations, stationary time series modeling is preferred, one which is independent of time.
2. Seasonality
By seasonality, we mean periodic fluctuations. We get to see different patterns in seasonality. For example, to witness seasonality in electricity consumption, we can see how the consumption is high during day time and low during night hours. Similarly, when the sales go up during Christmas or Black Friday and then slow down. To determine seasonality, one has to keep the autocorrelation plot in focus as well since it provides a sinusoidal shape.
3. Autocorrelation
Autocorrelation refers to the correlation or similarity between different observations due to the time lag between them.
Where do we use the time series model the most?
Time series analysis has widespread usage and application in:
• Rainfall measurements
• Automated stock trading
• Industry forecast
• Temperature readings
• Sales forecasting
How to build a time series model?
There are several ways of building a time series model or analysis for making predictions. The most prominent ones are:
• Moving average
• Exponential smoothing
• Double exponential smoothing
• Triple exponential smoothing
• Seasonal autoregressive integrated moving average (SARIMA.
Why do businesses require Time-Series Analysis?
Since time series modeling has a wide range of applications in different fields and walks of life, it is, therefore, instrumental to businesses. We see time-series analysis in stats, sales, economics, etc.
The reason why businesses use time series analysis are:
Features:
Time series modeling helps to track different features such as trend, seasonality, and variability.
Forecasting:
Time series analysis predicts stock prices quite accurately and tells if there will be an increase or decrease over time.
Inferences:
Businesses can predict the value infer from data points with the help of time series analysis.