Time Series Analysis with Python: A Comprehensive Tutorial

The abundance of data generated on a daily basis has made it simpler to monitor developing trends and patterns. One example of this is time-series data, which gets accumulated over a specific period. By studying such information, business analysts can make forecasts for the optimal timing of investments, production cycles, or demographic estimates, such as population growth. Some examples of data that are commonly analysed to make these predictions include stock market prices and sales figures.

The definition of time series analysis is:

Whenever the value of data transforms over time, it is termed as time-dependent. Forecasts about the future can be made by examining the past data and observing recurring trends. This prediction methodology incorporates both a dependent variable tied to time and an outcome to forecast future events.

Time series is a set of data points acquired at regular intervals over a definite duration. The independent variable in a time series pertains to time itself, enabling us to investigate any established patterns. Python is commonly used for conducting time series analysis to disclose valuable data patterns and relationships over time.

For instance: Over time, as businesses gain experience and build a database of information, they can leverage this knowledge to precisely predict when particular actions must be taken. It is crucial to have the right strategies in place in peak business seasons. In addition, companies must be equipped to meet customer demands for specific products and ensure that the supply is readily available. Time series analysis can assist in predicting these occurrences with greater accuracy, enabling corporations to make well-informed decisions and take necessary steps.

The components of temporal pattern analysis:

  1. Trend

    Changes in data over time or frequency can expose trends. This can indicate whether the data has increased or decreased, revealing insight into how the data has been altered. Population growth, market dynamics, technological advancements, and time can all cause variations in data patterns. Foreseeing these trends can be advantageous in predicting future changes and making informed decisions.
  2. Seasonality

    The recurring cyclical variations that take place throughout the year, such as holidays, changes in weather patterns, and other events, are referred to as seasonality. Typically, these patterns are predictable, which means that the outcomes of these events will likely show comparable results each year.
  3. Irregularity

    Irregularity in time series data pertains to the absence of established trends or predictable patterns. These temporal shifts manifest unexpectedly, triggered by unforeseeable events like natural disasters.
  4. Cyclic

    Oscillations that persist for more than a year in a time series are referred to as cycles. These variations can either be consistent or sporadic.
  5. Stationary

    A time series is labelled as stationary when it maintains consistent properties across the entire series. For instance, the variance, mean and covariance will remain unchanged throughout the series, which is a prerequisite for time-series analysis. Therefore, a series must consistently maintain these characteristics over time to be deemed stationary.

The ARIMA Technique

Time series analysis is a commonly used statistical technique that utilises the Autoregressive Integrated Moving Average (ARIMA) model to anticipate future values based on prior values and to identify any errors that may have been made during the forecasting process. ARIMA models improve the precision of future predictions and provide insights into past forecasting errors.

Autoregression-based Modelling

When historical and future data are interrelated, the autoregressive model is capable of making predictions based on prior values.

Moving Average with Shifting Values

A statistical approach known as moving average can be used to decrease fluctuations in a dataset. This technique operates by calculating the average of a particular range of data points over a specified timeframe. The moving average is computed by calculating the mean value of all the data points in the set.

When performing calculations, considering all available data points is crucial. After accounting for all available data points, an average must be computed. To establish a new average, the previous average should be subtracted from the first number in the series, then add the second number in the series.


In order to ensure that a time series remains consistent, an integration model is utilised to determine the disparity between latest and past measurements.

The Autoregressive Integrated Moving Average (ARIMA) model makes use of these distinct numerical parameters as individual components. These parameters are not only valuable in ARIMA modelling, but they can also be used to represent a variety of other models and operations. Below are the limitations of these parameters:

  • p: Lagged values derived from the autoregressive model at each previous time point.
  • q: The time lag in data for the error element derived from the moving average.
  • d: The frequency of integration and the frequency of altering data in order to ensure consistency.

ARIMA vs ARMA: Understanding the Distinction

The Autoregressive Moving Average (ARMA) model results from the fusion of Autoregressive (AR) and Moving Average (MA) forecast models. It generates a poorly stationary process that is formed by a combination of an Autoregressive (AR) polynomial and a Moving Average (MA) polynomial. To predict constant time series, ARMA is the recommended method, whereas to forecast both constant and non-constant series, Autoregressive Integrated Moving Average (ARIMA) can be employed.

The Autoregressive Integrated Moving Average (ARIMA) model leverages autoregression to ascertain the predictive capability of preceding values to anticipate future values. Furthermore, moving average is utilised to assess drift and predict future data points.

Let us acquaint ourselves with the distinguishing feature of ARIMA:

  • logarithmically arranged number of observations
  • “d” = “degree of differentiation” = “number of times the raw observations are differenced”
  • q = MA order = MA window size

ARIMA: Implementing the Technique

  1. Below are the instructions on how to employ the ARIMA model.
  2. Present the data in the form of a plot of time series.
  3. Ascertain the change in the mean after accounting for trend adjustment.
  4. Employing the logarithmic transformation will stabilise the variable.
  5. Note down the various log transformations to maintain the consistency of the mean and variance.
  6. To identify potential autoregressive and moving average models, create a plot of ACF and PACF.
  7. Discover the ARIMA model that is most effective for your data.
  8. Forecast the future value using the ARIMA model.
  9. To ensure no data loss, display the ACF and PACF of the residuals of the ARIMA model.


Analyzing Data Series in Python with Imports

Time series data can be obtained from spreadsheets in different formats, including .csv files. These spreadsheets generally feature two columns: the first column for the date and the second column for the measured value.

To import the time series dataset, we can utilise the read_csv() function in Pandas. If we add the parameter parse_dates=[‘date’], it will recognise the date column as a date field.


from dateutil.parser import parse


import matplotlib as mpl
import matplotlib.pyplot as plt


import seaborn as sns
import numpy as np
import pandas as pd


plt.rcParams.update({‘figure.figsize’: (7,3), ‘figure.dpi’: 110})


df = pd.read_csv(‘https://company.com/dataman/datasets/master/s10.csv’, parse_dates=[‘date’])

It is possible to use the date as an index by specifying it within a Pandas algorithm, and then importing it into the system. It is important to specify the index column, although there is a difference of opinion about which column to specify within the pd.read_csv() function.


ser = pd.read_csv(‘https://company.com/dataman/datasets/master/s10.csv’, parse_dates=[‘date’], index_col=’date’)

In the first example, the value of the column is higher than the date, which indicates that it is most likely part of a series.

What is Panel Data and How is it Utilised?

Data gathered in a panel study is typically collected over a longer period, differentiating it from other types of time series datasets that only measure one variable at consecutive time points. In contrast, panel studies involve collecting multiple variables over time, giving a more complete overview of the phenomenon being studied.

Panel data often includes explanatory factors in the present that may be helpful in predicting the value of Y, by supplying columns that contain data regarding the future forecasting period.

Methodology Based on Moving Averages

One of the most commonly utilised methods for analysing time series data is the moving average. This approach is effective in capturing the underlying trend of a series, while disregarding short-term fluctuations that may be present. Specifically, the rolling mean, or moving average, is calculated by averaging the k most recent data points in the series.

Types of Simple Moving Averages

This section will outline the three most frequently used types of moving averages:

1. SMA (Simple Moving Average): The Simple Moving Average (SMA) is a kind of weighted average that considers previous data points. The level of weighting can be adjusted based on the desired outcome; if a smoother average is preferred, a higher weighting may be applied, although this could come at the expense of accuracy. This type of analysis usually employs sliding window datasets, as it enables efficient data analysis.

2) EMA (Exponential Moving Average): The Exponential Moving Average (EMA) is a commonly employed method for identifying trends and removing extraneous data fluctuations. This technique assigns more weight to more recent data points than to earlier ones, making it more responsive to changes in the data and therefore more reactive than the Simple Moving Average (SMA).

3. CMA (Cumulative Moving Average): The CMA is the simple average of all values up to the present moment.

Applications of Time Series Analysis in Data Science and Machine Learning

Time series analysis is a potent mechanism within the domains of data science and machine learning, providing a range of modelling options. In an ARMA (Auto-Regressive Moving Average) model, the parameters p, d, and q refer to the auto-regressive log, differentiation order, and moving average lag, respectively.

The Autocorrelation Function (ACF)

The Autocorrelation Function (ACF) can be used to evaluate the similarity between current values in a time series and their past counterparts, as well as the correlation between values at two distinct intervals. The statsmodels package in Python can be employed to calculate autocorrelations, which can be useful in identifying patterns in a dataset and considering the impact of previous values on current data.

The Partial Autocorrelation Function (PACF) of a Sample Set

Even though the Autocorrelation Function (ACF) may be relatively simple to grasp, the Partial Autocorrelation Function (PACF) can prove more difficult to comprehend. PACF displays the correlation between the elements of a sequence, while accounting only for direct influences and considering a fixed number of elements in each sequence. Any events that could have a ripple effect in the middle of the sequence are filtered out.

Comparing ACF and PACF: Similarities and Differences

The present temperature can be seen to be impacted by the temperature that preceded it. However, as time elapses, this impact gradually diminishes, and the effect of the current temperature grows stronger. This serves to demonstrate the impact of temperature over a period of time.

Interpreting ACF and PACF Plots

It is crucial to note that analyzing both the Partial Autocorrelation Function (PACF) and Autocorrelation Function (ACF) requires a continuous time series. An Autoregressive model is a straightforward method of predicting the future by examining the present and past values. This technique is beneficial for forecasting the relationship between two time series values and how they will change with time.

An autoregressive model incorporates data from preceding time periods into a linear regression model to generate predictions for future outcomes. The Scikit-learn library simplifies the construction of the linear regression model, requiring only the input of desired parameters. The statsmodels library is consulted to ensure result quality and to determine the most appropriate lag values. The AutoReg class within the statsmodels library makes it straightforward to obtain the desired outcome with minimal effort.

  • Incorporating the AutoReg() Method into a Model
  • Using Fit() and Testing it with the Dataset
  • An Object is the Outcome of the Auto-Registration Process.
  • Making Accurate Predictions Using the Predict() Method.

Steps and Procedures

Time series analysis and forecasting have gained significant traction in the realm of deep learning, owing to their ability to effectively tackle issues that more conventional machine learning methods cannot. This technique is highly advantageous in resolving intricate problem statements.

Given the complex nature of time series forecasting, Recurrent Neural Networks (RNNs) have become the standard design choice. RNNs incorporate layers of input, hidden, and output neurons, with each neuron linked to the same constant time step across all layers. It is important to note that all layers follow the same processes, as they are all connected to a ‘secret’ layer. The direction of transmission of the hidden layers is determined by the flow of time.

The following is a detailed breakdown of the key elements of RNN:

  • Input:

    At time t, the input is a functional vector, x(t).
  • Concealed Information:

    The hidden layer at time t is denoted by the function h(t). This function considers the current input and the previous hidden state, enabling the network to store and retrieve past data. Essentially, the hidden layer functions as the network’s memory.
  • Outcome:

    Upon completing a certain processing duration, a function will generate a vector of results known as y(t).
  • Numerical Assessments:

    Weight U is applied to the input vector at time t, which is linked to the hidden layer of neurons.

Time series analysis is a potent tool with distinct capabilities that can be leveraged to make more precise predictions over time. It is an efficient approach that demands minimal effort to develop intricate designs. Despite requiring greater computational power for processing, the outputs are delivered swiftly and accurately.

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