A Gentle Introduction of Important Terms in Time Series Analysis

Time-series data is everywhere such as sensor streams from Internet of Things with respect to traffic to the stock prices of each company in finance domain. As I recently started delving into time-series data, I found it is interesting and there are many new terms and methods to learn.

In the following, we overview important things and terms before delving into details of time-series analysis and forecasting.

Despite the data is relatively simpler compared to unstructed data such as images or natural langues, time-series analysis is very challenging.

First of all, the predictability of a time-series depends on those checklists below:

• how well we understand the factors that contribute to the forecasting
• how much data is available
• whether the forecasted results impact the thing we would like to forecast

Secondly, we have to make it clear with end users who will use the forecast with respect to:

• what should be forecasted?
• what is the forecasting horizon? (e.g., forecasting 1 month in advance or 6 months in advance)
• how frequently are forecasts required? 

Important Terms

Variable of interest is the variable we want to forecast.

Predictors are other variables used for predicting/forecasting the variable of interest.





Trends exist if there is a long-term increase or decrease in the data.

Seasonality exists when a time series is affected by seasonal factors such as the time of the year or the day of the week. Seasonality is always of a fixed and known frequency.

Cyclic occurs when the data exhibit rises and falls that are not of a fixed frequency.

Stationary: A stationary time series is one whose properties do not depend on the time at which the series is observed. This means that time series with trends, or with seasonality, are not stationary. (How to do stationary test in Python? KPSS statistical test)

Differencing is a technique to compute the differences between consecutive observations (e.g., $y_t'=y_t - y_{t-1}$) and usually is being used for making a non-stationary time series to be a stationary time series. Differencing can help

• stabilise the mean of a time series by removing changes in the level of a time series, and
• therefore eliminating (or reducing) trend and seasonality.

Sometimes, second-order differencing is required to make the time series to become stationary.

$y_t'=y_t' - y_{t-1}'$