Time Series Handbook: Exploring Time Series Analysis for Data Scientists

Time series analysis is a type of machine learning that deals with time-ordered data. While solving the machine learning problems with machine learning algorithms , Not always we should expect the the data which is not having the time coupled features. 

Time series analysis consists of techniques dealing with these types of data, where different variables and features are analyzed and predicted according to their sequence and time in the dataset. 

In this article, we will discuss time series analysis, what it is, what is the core intuition behind it, how it works, some important terms related to time series, and components of time series analysis and prediction

Before directly jumping into any other technical part, let us discuss how time series analysis works. Below is the table of contents for this article.

What is Time Series Analysis?

In machine learning, we mainly have either time order or disordered time data. The time-ordered data is the type of data with a variable time in it and has an effect of time on the other variables, too. 

In contrast, time-disordered data is the type of data that does not include time and where time does not affect other variables and features of the data. 

Example Of Time Ordered Data

Example of Time Disordered Data

Now when we have time-disordered data, it is very easy to load, clean, preprocess and feed the data to the machine learning algorithm and build a model. 

But, when the time factor comes into the data, the data becomes somewhat complex, and here we can not use the traditional approach of building a machine learning model. 

So we need a particular type of machine learning technique, which is time series analysis.  

Now, for example, we can take any transaction data of any shop or shopping mall.

Here in the case of shop transaction data, we will have data on the sale of every product that has been sold out to the customers, but here we will also have a time variable, which will tell us which product has been sold out at what time. 

Now that we have an idea of time series, let us discuss the type of time series.

Time Series Types 

In time series analysis, we mainly deal with time-dependent data. According to the type of data and our requirement from the model, we can use time series analysis in multiple ways to get valuable insights from the data and generate revenues for the businesses.

Below are the some of the major areas where the time series analysis techniques are using.

Forecasting: 

In the forecasting type of model, we basically forecast or predict for the future. For example, predicting the sales of the shop for the next seven days based on the previous data.

Exploratory Data Analysis: 

Analyzing the data that is time-dependent in a visual form. This analysis can give us an idea about the different features and their trend over a period of time.

Classification of the Data:

Here we basically classify the different categories of the data. It is almost the same as supervised learning; we have time-dependent data here. 

Segmentation:

Here we divide the data into segments, and the data points are clustered and bunched in different segments according to their values in properties. 

Descriptive Analysis:

It is a type of time series analysis where we study the data and check the behaviour of the data, patterns in the dataset, and different cycles, seasons, and trends of the dataset. 

Explanative Analysis:

It is a type of time series where we study the dataset parameters and variables of the same. Adhere, we study how one variable is related to another by implementing correlation and covariance graphs.

We also study the causes and effects of one variable on other variables and the target variables. 

Intervention Analysis:

Here we study how one event or a change can change the output of the model and how the different variables of the dataset change and affect the target variable. 

We could use the data and time series analysis in practical ways to get valuable information from the data and create accurate, reliable models. 

Still, 4 key components in time series should be known to one to have a clear idea about the data and to process the data further to build a successful model.

4 Key Components of Time Series

There are mainly four components of the time series. 

1. Trend
2. Seasonal
3. Cyclical
4. Irregular

Trend

Seasonal data is a type of time series data that increases or decreases over a particular fixed interval of week, month, year, or decade. The interval or the time when the data values increase or decrease is known as the season. 

Seasonal Example: The sales of summer wear that has higher sales in the season of summer and lower sales in the case of winter. 

Cyclic

Cyclic type of data in time series is data that has data patterns that repeat over a time but not at the same time interval. The data pattern will be repeated but at different time intervals in the future. 

Cyclic Example: Ups and downs in the stock market. 

Irregular

Irregular is a type of time series data with no pattern or season in the data, where the data does not repeat, and where there is no correlation or pattern in the time series data. 

Irregular Example: Sales of video games.

Now that we have an idea about the key components of the time series, let us discuss the stationarity of the data in time series analysis, as it is one of the most important terms to have an idea about in time series analysis.

What is the Stationarity in Time Series Analysis?

In time series, stationarity is one of the most important terms used. It refers to the variables that do not change over time. 

In simple words, stationarity is a type of property that a variable can have, where the variable or the value of the same does not change across time and remains constant. 

The stationary time series has constant properties that do not change across time and remain constant, for example, mean and variance that remain the same across all time intervals. 

Opposite to the stationary type of data, we also have the non-stationary type of data in time series, which does not have constant variance over a period of time. 

Let us discuss the same in the next section, comparing the stationary and nonstationary types of data.

Stationary Vs. Non-Stationary

As we can see in the above image, the stationary data is almost equal to the same variance across all the time intervals. In contrast, the nonstationary data has rough or varying variance over time. 

Some of the time series models and algorithms need stationary data that does not change across time to make accurate and reliable models. That is why it is very important to have or convert the data into a stationary type in order to feed it to the model and build a model. 

The time series based on linear regression requires stationary data where it can predict only that type of data. Otherwise, the model will predict wrong, and it will not be reliable. 

Strict stationarity refers to the stationarity that does not change over time and remains very constant, whereas weak stationarity allows properties to vary but only to some extent. 

The time series analysis works best on the stationary data type; hence, converting the data into stationary is always recommended if it's non-stationary. Let us discuss how we can convert the non-stationary data into stationary.

How to Convert Non-Stationary Into Stationary Data

There are several ways to convert nonstationary data into stationery.

Differencing

In this type of method, we basically take the difference between 1 or more observations of the dataset. We subtract the previous observation of the dataset into the current observation and which is continued.

As we can see in the above image, we have applied second-order differencing on the dataset where we subtracted the observations from each other at the level of 2 row-wise. It converted the data into stationary.

Transformation: 

This is a type of method where we transform the data and, ultimately, the distribution of the data. We have three types of transformers:

1. Function,
2. Power,
3. Quantile. 

In the function transformers, we can use log transform or square root transform, and also, in power transform, we can use box-cox and yeo-johnson transform.  

Function transformers are the most commonly used transformation techniques.

Detrending: 

As the name suggests, it is a type of method where the trend from the dataset is removed, and only differences from the observations are shown. 

Although time series analysis is a great thing that can be used to study time-dependent data, it also has certain limitations. Let us discuss a bit about those.

Assumptions to consider before working on Time Series Problems 

1. Stationarity: The data should be stationary before applying time series analysis to it. If the data is not stationary, then we should consider converting it to the stationary type of data. 
2. Distribution: the data distribution should be normal or approximately normal. If it is not, then we can apply transformation techniques to make the distribution of the data normal. 
3. Amount of Data: The data that we are using for time series analysis should be enough. We can still use time series analysis if we have very little data, but it would not be that effective and accurate. 
4. Independence: The variables of the dataset should be independent of each other in the data we are using for time series analysis. In other words, no multicollinearity should be present in the dataset. 
5. Homoscedasticity: Homoscedasticity refers to the constant variance or spread of the data. The error terms in the time series analysis should have this constant variance, or it should be homoscedastic. If the error terms are heteroscedastic, the results or predictions of the model can be biased or incorrect. 

Limitations of Time Series Analysis

Three are several limitations of the time series analysis:

1. Amount of Data: The time series analysis requires a significant amount of the data to be trained and analyzed on. 
2. Outliers: Time series analysis is very sensitive to outliers, so outliers must be identified and handled before applying them. 
3. Stationarity: The data we use for time series analysis needs to be stationary; if not, it should be converted to stationary data. 
4. Autocorrelation: The time series that we apply assumes that the observations of the dataset should not have any autocorrelation. Hence, the observations of the dataset should not be correlated with each other. 
5. Missing Values: The time series analysis does not support missing values, and hence it should be imputed or dropped.

Key points to Remember

1. There are mainly two types of data, time-ordered and time-disordered data. Time-ordered data is the type of data where it includes a time variable in it and has the effect of the same.
2. We can use time series analysis in forecasting, classification, exploratory analysis, text analysis, descriptive analysis, segmentations, etc. 
3. Trend, cyclic, seasonal, and irregular are the four main key components of the data.
4. Stationarity is a term used in time series that denotes the data's constant value over time. 
5. Different differencing, detrending, and transformation techniques can be used to convert the nonstationary data into the stationary data type. 
6. Time series analysis has certain assumptions where the data should be stationary, there should be no missing values, and no autocorrelation between the variables of the data.

Conclusion

In this article, we discussed the time series, what it is, the core idea behind the time series, the different models used in the time series, the four main components of the time series,  the stationarity of the data, the difference between stationary and nonstationary data, methods to convert the data into stationary, and the assumptions and limitations in time series analysis. 

This article will help one to understand and start with time series and will help to understand the very basic concepts and fundamentals of time series.

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