Moving Averages for Smoothing Time Series Data

Moving averages or MAs are useful for smoothing time series data, unearth underlying trends and determine components used in statistical modeling. Smoothing refers to eliminating random variations that might appear as roughness in a raw time series data plot. It minimizes the noise to enhance a signal that may show cycles and trends. Analysts also know the smoothing process as filtering the data.

MA was developed in the 1920s and is the oldest known process used for smoothing data today. The method depends on the idea that observations that are close in time are more likely to have similarities in values. The averaging eliminates the noise of random variation from the data.

Time Series Forecasting

Forecasting via time series uses data using historical values and related patterns to predict any future activity. This is mostly related to trend analysis, cyclical fluctuation analysis, and seasonality. Success is not a guarantee with this method as with any forecasting technique.

Components of Time Series

The things affecting an observation’s values in a time series are its components. They include:

  • Trend
  • Seasonal and cyclic variations
  • Random movements
  1. Trend

The trend indicates the general data tendency to decrease or increase over a long period. A trend is a smooth, average, and long-term tendency. It is not absolute that the decrease or increase is in one direction over the given time frame. A tendency may decrease, increase, or stay stable in different time frames. However, the overall trend must be either stable, downward, or upward.

2.    Seasonal and cyclic variations

Seasonal and Cyclic Variations are defined as periodic or short-term fluctuations.

3.    Random Movements

Some movements are irregular and random. They are uncontrollable, unexpected, and erratic.

How to Analyze Time Series Data

You can use statistical techniques to analyze time series data in two ways. One is for generating inferences on how variables affect other variables on interest over a period and how to forecast any future trends.

What are Moving Averages?

Moving averages or MAs are a series of averages that are calculated via sequential data point segments on a series of values. Their length defines the data points that you should include in each average.

·         One-Sided MAs

One-sided MAs are inclusive of both the current and previous observations per average

·         Centered MAs

Centered MAs include the previous and future observations and calculate the average at a particular point in time. The centered MAs use observations surrounding it in both directions and are also referred to as two-sided MAs.

Centered intervals pan out evenly for odd observation numbers as they allow an even number of observations before and after the MA. If you have an even length, the calculations adjust and vary by using a weighted MA.

How Moving Averages Reveal Trends

MAs can eliminate seasonal patterns and reveal any underlying trends. If you need to eliminate seasonal data in your data, you must set the length of your MA to be equal to the length of the pattern. If you don’t have any seasonal data, select a reasonable length. The longer the length, the smoother the lines. Seasonal data in this context has nothing to do with literal seasons. It refers to a repetitive pattern in your data with a fixed length.

The downside to using MAs to smooth data series is that these calculations rely on historical data. This reliance on historical data means the timeliness of the variables is lost. This is one reason why you should use a weighted MA as the variable’s current values are accorded more importance.

Data Expectations

Calculating an MA of a time series makes some data assumptions. It assumes that both the seasonal and trend components have been eliminated from the time series. This makes your time series static and does not indicate any obvious trends such as decreased movement or seasonality.

You can use various methods to eliminate seasonality and trends from time series data when you are forecasting. Two of the best methods are the differencing method, modeling the behavior and removing it from the series.

Moving Average as a Data Preparation Technique

MA can be used as a data preparation method for creating smoothed versions of the original data. Smoothing helps to minimize the random variations and expose the underlying process’s structure.

Conclusion

You can use moving averages for smoothing time series data or eliminating random variations to give a smoother data plot. This smoothing process is also known as filtering and is a useful way of revealing underlying market trends.