What is Timeseries Data?

Time series is a sequence of observations recorded at regular time intervals. Time series analysis can be useful to see how a given asset, security, or economic variable changes over time. Another big question is why we need to deal with missing values in the dataset and why the missing values are present in the data.

• The handling of missing data is very important during the preprocessing of the dataset as many machine learning algorithms do not support missing values.
• Time series are subject to missing points due to problems in reading or recording the data.

Why can’t we change the missing values with the global mean because the time series data might have some seasonality or trend?  

Conventional methods such as mean and mode imputation, deletion, and other methods are not good enough to handle missing values as those methods can cause bias in the data. Estimation or imputation of the missing data with the values produced by some procedures or algorithms can be the best possible solution to minimize the bias effect of the conventional method of the data. So that at last, the data will be completed and ready to use for another step of analysis or data mining. 

Types of Time Series Data

Let’s start by categorizing time series data based on its composition before delving into imputation methods. If we use a linear regression model to break down the time series data, it can be represented as:

Here,

• represents the trend,
• represents seasonality, and
• represents random variables.

Based on the presence or absence of these components, the passage identifies four types of time series data:

1. No trend or seasonality (Constant): Data remains relatively constant over time, with neither trend nor seasonal fluctuations.

2. Trend, but no seasonality (Trendy): Data exhibits a clear long-term trend (increasing or decreasing) but no regular seasonal patterns.

3. Seasonality, but no trend (Seasonal): Data shows recurring fluctuations within a specific period (e.g., monthly sales cycles) but no overall trend over time.

4. Both trend and seasonality (Trend-seasonal): Data exhibits both a long-term trend and recurring seasonal patterns. This is the most complex type of time series data.

Types of Missing Data

Missing data is a common challenge in time series analysis, impacting the accuracy and reliability of your results. Understanding the different types of missing data is crucial for choosing the right imputation strategy to address them effectively. Here’s a breakdown of the main types:

1. Missing Completely at Random (MCAR): Data points are missing randomly and independently of any other variables or observations. This is the ideal case for imputation, as any method can be used without introducing bias.
2. Missing at Random (MAR): Data points are missing depending on observed values in other variables, but not on the missing values themselves. This is a more complex scenario, but imputation using observed data can still be effective.
3. Missing Not at Random (MNAR): Data points are missing depending on the missing values themselves, making them difficult to predict accurately. This is the most challenging case, as traditional imputation methods can introduce bias and distort your analysis.

It is common to come across missing values when working with real-world data. Time series data is different from traditional machine learning datasets because it is collected under varying conditions over time. As a result, different mechanisms can be responsible for missing records at different times. These mechanisms are known as missingness mechanisms. In this article, we will discuss how to handle missing values in time series data using Python.