What is Time Series Analysis?

Time Series Analysis and Forecasting Overview

Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time.

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Alpha Crunching uses the close price of SPX at 5m intervals for the Daily and Weekly Forecasts.

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What sets time series data apart from other data is that the analysis can show how variables change over time. In other words, time is a crucial variable because it shows how the data adjusts over the course of the data points.

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When analyzing data over consistent intervals, we can also use time series forecasting to predict the likelihood of future events. Time series forecasting is part of predictive analytics. It can show likely changes in the data, like seasonality or cyclic behavior, which provides a better understanding of data variables and helps forecast better.  

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For our purposes, the time series analysis/forecasting we will be exploring for trading will involve the seasonality effect of the data. I don't personally like to use the term seasonality for what we will be exploring because it invokes the idea of seasons which is a set of data that spans an entire year.

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The sets of data we will be using will only be spanning a few weeks at 5m intervals. So to me, seasonality isn't the best word for it. We could consider calling it intra-day seasonality or keep it simply as time series analysis and/or forecasting.

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So just know that for our purposes, these terms may be used interchangeably.

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To learn more about time series analysis, check out this Wikipedia page on what a Time Series is. As you'll find, there are many ways to use time series analysis across various industries as well as many complex mathematical concepts that can be involved.

https://en.wikipedia.org/wiki/Time_series

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