What is Time Series Analysis and How it Can be Used to Analyze and Visualize NFT Data: The New Frontier of NFT Data Analytics and Time Series Analysis
As we have argued in our previous articles, NFT data analytics is a major new frontier for cryptocurrency analysts and investors. This article outlines what time series analysis is and explains how it can be used to analyze and visualize NFT data.
Time series analysis is a fundamental part of understanding data that can be tracked chronologically. Plotting data on a time series chart allows us to visualize relationships between variables, changes over time, patterns, and unusual observations. This is often most appropriate in the “data exploration” phase of an analysis — and can give us clues as to the best approach to use to extract valuable insights from data. Forecasting, often referred to today as predictive analytics, may extend a time series visualization to demonstrate the best guess for the future values of the analyzed variables.
NFT data is rich in variables that are well-suited for time series analysis. Because the ownership and lifecycle of an NFT is built into a blockchain, the precise time and date of every on-chain transaction is immutably recorded. This removes (barring any issues with the blockchain in question) a common necessity in data exploration which is to assess the reliability of the time series and impute or correct for any missing values.
An individual NFT’s price is an obvious contender when searching for variables of interest, and every investor is familiar with charts that plot the price of an asset over time. For making predictions, though, it might be best to use aggregated variables that help to reduce the noise introduced by too close of a focus. In other words, factors that introduce bias in the data of a single NFT will likely have less of an impact on a larger set of NFTs.
Price analysis would become the mean price or median price analysis of a set. Transaction analysis could also use an average, or could look at the sum of the transactions in the set. Best practices would include discarding outliers as a reliable way of further reducing bias.
So, we’ve acquired our data, identified the variables of interest, tidied up the outliers, and plotted the series. Are we ready to buy and sell NFTs for massive profit yet?
Well… no. Predictions based on the appearance of a time series plot falls into the realm of technical analysis — an investment strategy that has been thoroughly debunked (depending on who you ask). Statistically sound predictions will come from more advanced forecasting methods — dynamic regression models, neural networks, the Prophet model, etc.
These advanced methods build on the insights that a time series analysis gives us. For example, one of the variables of Facebook’s Prophet model is s(t), or the seasonal component. Seasonality in a time series describes the way patterns in some data can appear over a set time interval. For example, the rise in retail sales before the holidays every year is a reliable seasonal pattern. Seasons don’t have to be yearly or even monthly. With NFT data, especially NFTs with a lot of activity, a season could be a week or even a day. Perhaps you have the hypothesis that auctions for a particular set of NFTs are most often initiated on Monday morning — a seasonal plot could provide some evidence of that.
While we would all like a model or algorithm or advanced “AI” that reliably predicts NFT prices so that we can trade perfectly until the day we Scrooge McDuck into a swimming pool of cash — organizations and individuals with unfathomable resources still have not accomplished this. Rather, time series analysis and subsequent models and analyses can help us build a quantitative understanding of the NFT space and become one of many tools the smart investor uses to identify unusual activity (which may represent an opportunity) and make informed decisions.
Join us at Defy Trends as we roll out NFT analytics tools and more.
Defy Trends is an intuitive, all-in-one crypto and NFT intelligence platform that empowers individual and institutional investors to make confident data-backed investment decisions. The Defy Trends platform includes sentiment analysis, the Defy Trends Score news aggregation, on-chain analysis, AI forecasts, in-depth educational materials, and crowd-sourced research.
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