Discussing the article: "Data Science and ML (Part 34): Time series decomposition, Breaking the stock market down to the core"
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: Data Science and ML (Part 34): Time series decomposition, Breaking the stock market down to the core.
In a world overflowing with noisy and unpredictable data, identifying meaningful patterns can be challenging. In this article, we'll explore seasonal decomposition, a powerful analytical technique that helps separate data into its key components: trend, seasonal patterns, and noise. By breaking data down this way, we can uncover hidden insights and work with cleaner, more interpretable information.
Trend
The trend component of the time series data refers to the long-term changes or patterns that are observed over time.
It represents the general direction in which the data is moving. For example, if the data is increasing over time, the trend component will be upward-sloping, and if the data is decreasing over time, the trend component will be downward-sloping.
This is familiar to almost all traders, the trend is the easiest thing to spot in the market by just looking at the chart.
Seasonality
The seasonal component of a time series data refers to the cynical patterns that are observed within a given period of time. For example, if we are analyzing monthly sales data for a retailer who specialized in decoration and gifts, the seasonal component would capture the fact that sales tend to peak in December due to Christmas shopping, sales flattens once the holiday season is over, in months January, February, etc.
Residual
The residual component of a time series data represents the random variation that is left over after the trend and seasonal components have been accounted for. It represents the noise or error in the data that cannot be explained by the trend or seasonal patterns.
To understand this further, look at the image below.
Author: Omega J Msigwa