When recording data, one thing is of utmost importance: time.
When was the data recorded? How much time passed from one interval to the next? In time series analysis, time is the most important factor, as the technique depends on analyzing data points collected at regular intervals over a determined amount of time.
What Exactly is Time Series Analysis?
Time series analysis, or TSA, uses time series data to demonstrate how certain factors change over time. As mentioned before, consistency is key as it sets apart time series data from randomly recorded data, and it’s how you can use TSA to extract meaningful statistics and insights.
TSA can be used to understand patterns over time, such as average monthly temperature or a business’s quarterly profit. For more advanced use cases, you can use time series data to perform time series forecasting and predict future trends and make informed decisions based on previously observed data, such as financial market performance or electricity consumption.
Now that we know what time series analysis is, let’s look at how organizations can use it effectively, and why it’s so essential for everyday operations.
Why Is Time Series Analysis Important?
Organizations use time series analysis to see and understand changes in data over time. Using time series analysis makes it easier to see trends, patterns, and outliers in a set of data. It can also be used to show cyclical or seasonal changes in data due to recurring behavioral patterns, which providers a better understanding of why these behaviors occur and how to plan for them in the future.
Here are a few real-world applications of time series analysis:
- Stock traders use TSA to gain a better understanding of how stock prices vary over time.
- Retailers use TSA to understand how the holiday season influences consumer shopping behaviors.
- Supply chain distributers use TSA to analyze traffic patterns and determine the best time to transport products.
Time series forecasting takes time series analysis to the next level. Users can take historical data and make predictions about what will happen in the days, weeks, and months to come, so they can effectively plan and make the best decision for the business.
Major Components of Time Series
There are a few factors that can cause variations in time series data. The following five components are used to describe how time series data behaves:
Autocorrelation refers to the relationship between a given observation in a time series and a previous observation in the same time series, where the interval between the two data points is referred to as a “lag.” Analysts use autocorrelation functions to understand if the connection between two lags is significant and to determine how random or stationary the time series is.
When data experiences predictable changes at regular intervals such as quarterly, monthly, or biannually, it’s referred to as seasonality (whether those are meteorological seasons, holiday seasons, or other). For instance, summer clothes are sold more in the spring than in other seasons, and Black Friday is the busiest shopping day of the holiday season. Seasonality always occurs in a fixed and known period.
A trend represents a long-term movement of data in a certain direction. The trend can be increasing or decreasing or even linear or nonlinear. Not all series have a noticeable trend—things like fires, floods, revolutions, earthquakes, strikes, and epidemics are clear representations of this. That said, the overall trend must be upward, downward, or stable. Examples include periods of economic growth and recession, the average prices of apartment rentals in each city, and sales of a particular product.
Cycles occur when data shows a rise and fall pattern that is not over a fixed period. While many people confuse cyclical variations with seasonal variations, they’re quite different. Cyclical variations have nothing to do with the time of year and can’t be measured according to a given calendar month. They also typically last longer than seasonal variations and are often economic in nature. For example, monthly housing sales can reflect overall market trends, and demand rises and falls in a cyclical pattern over time.
An irregular component is due to short-lived fluctuations in a series. While it’s not predictable, sometimes irregularities such as sales tax changes can be anticipated. Irregularities represent the remaining time series outside the trend cycle and the seasonal components. Examples include natural disasters, health crises, and wars.
Modeling Time Series Analysis
There are several ways to model a time series analysis to make astute predictions. The main types include moving average, exponential smoothing, and ARIMA.
Moving Averages (MA)
This technique is probably the most basic of all time series forecasting models. It states that an upcoming data point will be the mean of all the past data points. The main aim of this model is to identify and highlight trends and trend cycles. The longer the window, the smoother the trend.
Exponential Smoothing (ES)
Like the moving average, the exponential smoothing method is used for univariate series. Here, the upcoming data points are determined based on an exponentially decreasing average of past and current values. The older the value, the less weight is assigned to it. The exponential smoothing technique is used for short-term predictions.
There are three major types of exponential smoothing methods you can use depending on the seasonality and trend of the variable—the simple single exponential smoothing technique, the double exponential time series model, or (the most advanced) triple exponential time series model.
- The simple exponential smoothing technique is used for time series data that lacks seasonality and trend. Alpha (the smoothing factor that includes values between 0 and 1) is used when the current data lacks seasonality.
- The double exponential smoothing technique is used where there is a trend in the time series. In addition to the alpha parameter, beta (the trend smoothing factor that takes values between 0 and 1) is used to regulate the changes in trends of the series.
- The triple exponential method features three levels because of the seasonality and trend of the data. Therefore, apart from alpha and beta factors, this technique includes a gamma parameter (seasonal smoothing factor) to control the effect of seasonality in the series.
Autoregressive Integrated Moving Average (ARIMA)
This is another popular forecasting method that includes a combination of two or more time series analysis models. The ARIMA technique is ideal for multivariate non-stationary data. It’s based on the concept of moving average, autocorrelation, and autoregression.
When seasonal data is involved, a model known as SARIMA (seasonal ARIMA) is used. SARIMA is an extension of ARIMA that supports univariate data with both seasonality and trend components. It adds three hyper-parameters to specify the moving average, difference, and autoregression for the seasonal component of the data series.
Choosing the Right Time Series Analysis Solution
By understanding the key components of time series data, and implementing them accordingly, organizations can make sense of their past data and use it to make informed decisions based on past and predicted future trends.
RapidMiner makes conducting time series analysis easy. With our all-in-one data science platform, your team can use historic data you already have to optimize processes, make better use of your existing resources, and plan for a brighter future.
Ready to analyze your current data trends and put time series analysis to work for your company? Request a demo today to see how top-notch data analysis and data science can level up your operations.