Understanding Time Series Analysis
In this article, I will take you through a complete understanding of Time series analysis.
Time series analysis is considered an essential skill for various purposes, but mostly we neglect it in machine learning. A time-series is any series of data that represents the events of a particular time. This type of data often helps us in predicting future events by looking back into the past events. It is also fascinating to see that several industries use time series forecasting to solve various industry problems.
What is a Time Series?
Time series is a series or sequence of observations noted at regular time intervals.
A time series can be hourly, daily, weekly, monthly, quarterly and annual, depending on the frequency of observations. We can have seconds and minute-wise time series, even, for instance, the number of clicks and the user visiting every minute.
Why analyze a time series?
It is the essential step before developing a forecast of the series.
Besides this, time series forecasting has immense commercial importance. Like the stuff essential to a business, as demand and sales, the number of visitors to a website, stock price, etc., are crucial time series data.
Let’s understand the critical parameters of time-series analysis.
- Trend: The trend gives a usual direction of the time series data over a long period. A trend can be increasing(upward), decreasing(downward), or stationary.
- Seasonality: Data is seasonal if it changes precisely in the same manner over a different time period. The seasonality element shows a trend that repeats concerning timing, direction, and magnitude. Some of the time-series examples are, rise in water consumption during summer or an increase in airline passengers’ number during holidays each year. This is an essential assumption of time series data that should be true for successful forecasting.
- Autocorrelation: This is another essential parameter that assumes that the data necessitates being auto-correlated. Data is auto-correlated when it exhibits similar observations over a time lag present in the data.
- Cyclical Component: Cyclic components are the trends that do not repeat over a particular period of time. A cycle is described as the period of ups and downs, booms, and slums of a time series. It is mostly observed in business cycles.
- Irregular Variation: Irregular Variation is the fluctuations in the time series data which become apparent when trend and cyclical variations are eliminated. These variations are variable, erratic, and may or may not be random.
- ETS Decomposition
ETS Decomposition helps in separating different parts of a time series. Here, the term ETS stands for Error, Trend, and Seasonality.
Cases where we use Time Series Analysis :
- It is used to predict the number of incoming or churning customers.
- It is used to explain seasonal patterns in sales.
- It helps in detecting unusual events and estimating the magnitude of their effect.
- Time Series estimates the effect of a newly launched product on the number of sold units.
How to Check the Stationarity of a Time Series?
Time Series is stationary if its statistical properties, for instance, mean, variance remains constant over time. But why that’s important? Mostly time series models assume that the time series is stationary. Generally, we can say that if a time series has a particular behavior over time, there are very high chances that it will follow the same pattern in the future. Also, the theories related to stationary series are mature and easy to implement compared to non-stationary series.
Stationarity is explained using rigorous criteria. Although, for practical purposes, we can assume that the series is stationary. It should have constant statistical properties over time, i.e., mean, variance, and autocovariance(does not depend on time) should be stable.
Let’s understand the essential models for time series analysis.
Models for Time Series Analysis
Various models can be used to determine and predict data points in a time series. Here, we’ll discuss two of the most important models: moving averages and exponential smoothing.
A moving average model states that the upcoming data point is equal to the average of previous data points. This rudimentary model is robust in smoothing out data sets to estimate their overall trend, with little regard for outliers. But, it may smooth out the seasonality of some time-series data.
Exponential smoothing is also an important time series model. Here, upcoming data points are predicted based on an exponentially decreasing average of past data points. It is much preferred to a moving average model in time series when there is no clear trend or pattern.
Time series analysis is a complex data analysis area. It focuses on processing, describing, and forecasting time series; they are time-ordered datasets. Various models are employed to help explain time series, including moving averages and exponential smoothing models.
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