Steps to conduct time series analysis using moving averages

Time series analysis is a technique commonly used in machine learning to predict future trends based on past data. Among them, moving average is one of the most commonly used and powerful tools in time series analysis. Moving averages can effectively eliminate the volatility of data by averaging a set of values ​​within a specified time period, thereby determining the overall trend of the data. When predicting future values, moving averages provide a smooth trend in the data, helping us make more accurate predictions.

Simple moving average (SMA) and weighted moving average (WMA) are two commonly used forms of moving averages in time series data analysis. When choosing the window size for a moving average, you need to make an appropriate choice based on the frequency of the data and the level of smoothing required. When comparing a simple moving average to a weighted moving average, there are factors to weigh against smoothness and responsiveness.

Simple Moving Average

The Simple Moving Average (SMA) is a basic form of moving average that works by calculating the average of a set of values ​​over a specified time period. The window size of the SMA is usually chosen based on the frequency of the data, it needs to be long enough to smooth out fluctuations, but short enough to capture any trends in the data.

Weighted Moving Average

The Weighted Moving Average (WMA) is an advanced form of moving average that takes into account the impact of each value by assigning a different weight to each value. relative importance. In this way, WMA can reflect changes in data more sensitively. Specifically, WMA gives more recent data points higher weight, while older data points get lower weight. This weight distribution method allows WMA to better track the trend changes in the data.

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