Time Series Analysis using Python

In this article, we will learn about Time Series Analysis using Python. Let’s start with what is time series?

What do you mean by time series?

Time series stands for a simple collection of objects or things collected over a period of time. Consider the data points, and each of these data points is linked to a timestamp.
Examples may include the stock market and the changes in stocks, such as the rise and fall in prices over time.

Another example is the weather forecast of a given region and how the data points change when there is a rise or decrease in humidity or temperature. Analysing these patterns in data can help companies and organisations to make decisions about the future. These decisions would eventually lead to increased profits and overall productivity growth.

Need of Time Series

Data forms the centre of any organisation. Around the globe, companies exchange data in billions and over a while, it becomes challenging to track and manage this data. The time series gives you an expanded view of the data changes over time and makes it easier to understand and track your data.

You can also analyse changes in past data to predict the future. For example, time series is a set of observations that collect data over time. When plotting the time series in a graph, time is always one of the axes in the graph.

Time Series Analysis in Python

Time series in Python realises that over a while, the data forms a pattern, and thus, it uses time series characters to extract these patterns. These patterns help us better understand our data.

Consider the example of a florist shop. By looking at past patterns in data, the florist can better understand which flowers are sold most during a particular season and which are preferred for specific occasions. It allows the florist to grow and sell such flowers in the future. It is one simple example of the effect of time series on daily life.

Steps to Perform Time Series analysis using Python

First, we need to notice the stationary and correlation in the dataset. Stationary is the differential that occurs at regular periods, whereas correlation is the similarity of the future values based on the past values. You check both these values in the time series data set, and several methods are available to check for these. The ARIMA model is one such method.

Next, we must check for trends, perform the trend decomposition, and forecast future values. Decomposition helps you understand and picturise the trends and commonalities in your data which understands you to predict the values better.

Steps to Analyse a Time Series

1. Collection of data and data cleansing.

2. Visualisations of time vs the given key feature.

3. Observation of the stationary of the series.

4. Chart development to understand nature.

5. Model construction- ARIMA, AR, MA, and ARMA.

6. Insight extraction and understanding.

Uses of Time Series Analysis in Python

The primary use of time series is forecasting and prediction of future values. It helps in the following:

• Analysis of historic patterns in data.
• Assessing the current situation by taking information from past situations.
• Evaluating the factors influencing the time series over different time periods.

Time series helps in obtaining time-based results in the following.

• Forecasting
• Segmentation
• Classification
• Descriptive analysis
• Intervention analysis

Limitation of Time Series Analysis in Python

We must consider the following limitations of time series analysis in our analysis.

1. TSA does not support missing values.

2. It is expensive to perform data transformations.

3. Models work only on uni-variate data.

Data Types of Time Series

There are two primary data types in time series analysis in Python. They are as follows:

• Stationary
• Non-stationary

In stationary, the MEAN, VARIANCE, and COVARIANCE are constant, whereas it is the opposite in the case of non-stationary.

Methods to check stationary data type

We must check for stationary data in the dataset before performing the time series analysis. For this, we have the Statistical Test. We have two statistical tests to check for a stationary dataset. They are as follows:

1. Augmented Dickey-Fuller test.

2. Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test.

Augmented Dickey-Fuller Test

It is the most common test to check if the dataset is stationary or not, and it takes the following assumptions:

• Null hypotheses(h0): series is nonstationary
• Alternative hypothesis(HA): series is stationary
• P-value >0.05 rejects H0
• P-value <=0.05 accepts HA

Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Test

These tests calculate tests on the null hypothesis(h0). During time series analysis, the data set must be stationary.

Converting Non-Stationary into Stationary data type

The following are the methods to convert non-stationary data into stationary dataset values:

• Detrentreing
• Differencing
• Transformation

1. Detrending

This method only shows the difference in trends after removing the effects from the given dataset. Only cyclic patterns are noticed.

2. Differencing

Differencing is done by subtracting or removing the previous observation from the current observation. It helps in stabilising the mean to reduce trends and seasonality.

Yt=Yt-Yt-1
Yt= value with time

3. Transformation

Power transform, square root, and log transfer are the three methods used in transformation.

Components of time series analysis in Python

1. Trend

Trends give you a look into how your data grows and falls over a period of time. They show the difference in data patterns or frequency. It is because the data either rises, falls, or remains constant.

2. Seasonality

Seasonality discovers variations that occur at constant intervals of time. Examples include seasons and festivals. The variations occur during the same period over and over again and hence affect how we predict data.

3. Irregularity

These are random fluctuations or changes in the data patterns that do not rely on seasonality or trends. They may occur due to unpredictable changes, such as a hike in population due to natural accidents or global warming.

4. Cyclic

Cyclic oscillations continue for more than one year. Therefore, they don’t need to be periodic.

5. Stationary

If a time series has the same set of properties even after a duration of time, it is known as stationary. It is because the statistical properties are the same in the entire time series, and the data must be stationary for time series analysis. Furthermore, the mean, variance, and covariance are the same for any stationary series.

ARIMA model

ARIMA stands for Autoregressive Integration Moving Average. It forecasts errors in a time series and future values based on past values.

Autoregressive model

When there is some correlation between past and future data, the autoregressive model predicts future data values. The following formula is used to state the autoregressive model.

Y_t=w+ϕY_t-1+e_t

Here,

• Y_t is the target
• w is the intercept
• ϕ is the coefficient
• Y_t-1 is the lagged target and
• e_t is the error

Moving average Methodology

Moving average is a technique to smoothen out the data based on variations. It computes the average of different subsets of a given dataset. The new average helps us to reduce noise and distortion within the subset.

To calculate the moving average, we first calculate the average and then remove the first point only, and then we calculate the new average.

There are three types of moving average methods:

• Simple moving average.
• Cumulative moving average.
• Exponential moving average.

1. Simple moving average

Simple moving averages are easy to calculate. They can be calculated for different time periods. If the simple moving average goes up, security prices increase. If the moving average goes down, security prices decrease.

2. Exponential moving average

More attention is given to the recent data points in the exponential moving average. It is also known as the exponentially weighted moving average.

The conclusions from calculating the exponential moving average decide whether any business decision is profitable. We apply the moving average in the cases of trending markets. Based on the average, we can tell if there is an upward or downward trend in any business decision.

Integration

Time is one of the variables in the time series datasets. To make the time series stationary, we make use of integration. Integration is the difference between present and past values.

Conclusion

So in this article, we looked at a detailed view of the time series analysis in Python. We hope that it was insightful and easy to understand.