Introduction to Time Series Data Forecasting

Malav Champaneria 27 Feb, 2023 • 5 min read


Welcome to my first Timeseries data forecasting blog post! In the modern world, a sizable chunk of the data that is generated every day surrounds us and is in the form of time series. Data from a time series is typically produced at regular intervals and is sequentially organized. So, in this article, we are going to see how to do time series forecasting.

Time Series Data Analysis

It is used in various industries, including industrial engineering, finance, economics, and healthcare. Time series data analysis can be used to generate forecasts, find patterns, trends, and anomalies, as well as to detect trends and patterns.  Let us start from basic by knowing what the exact Timeseries term means.

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is Time Series?

A time series is a collection of data items that are periodically recorded and arranged in chronological order. Time series data can be employed to examine how a certain variable varies over time and forecast future patterns. Time series data examples include stock prices, meteorological information, sales numbers, and website traffic. To glean significant insights from the data, time series analysis employs a variety of statistical approaches, including trend analysis, seasonal analysis, and forecasting. Time series data is extensively employed in many disciplines, including finance, economics, engineering, and social sciences.

In order to estimate future values of a variable based on its previous behavior, forecasting time series data includes the use of statistical models and procedures. Time series forecasting aims to spot patterns and trends in historical data and utilize this knowledge to generate future forecasts.

For time series forecasting, a variety of methods and models are employed, some of which include:

  • Simple moving average.
  • Exponential smoothing.
  • ARIMA.
  • Prophet.
  • LSTM.

Above mentioned models are very basic for time series data forecasting and may not get you high accuracy in prediction. Apart from this, several more advanced machine learning models can help you get predictions with high accuracy and low MAE, MSE, RMSE, and MAPE.

Such models’ accuracy is assessed using performance metrics. The particular application and the demands of the anticipated variable will determine the performance measures to be used. For time series forecasting, the following performance metrics are frequently used:

  • The average absolute difference between forecasted and actual data is calculated using the mean absolute error (MAE) measure. It serves as a gauge for the size of forecast errors.
  • The mean squared error (MSE), which measures the average of the squared discrepancies between the predicted and actual values, is a measurement of error. It serves as a gauge for the typical magnitude of forecast errors.
  • Root Mean Squared Error (RMSE): This metric calculates the MSE’s square root and assesses the average magnitude of prediction errors.
  • Mean Absolute Percentage Error (MAPE) calculates the average absolute percentage difference between predicted and actual values. When the magnitude of the predicted variable is not constant across the entire data set, it is helpful.
 Regression losses

This was all about performance metrics which help to assess models. Now we should know the basic difference between the Timeseries dataset and the normal dataset, though we have to predict values, i.e., forecasting, now some factors may vary.

A time series dataset features temporal ordering of the data points, which is the main distinction between it and a normal dataset. In other words, a time series dataset contains data points that are consistently spaced apart by minutes, hours, days, months, or years.

An analysis of patterns, trends, and seasonal fluctuations can be done using the time-dependent structure resulting from the temporal ordering of the data points in a time series dataset. However, the data points in a normal dataset can be thought of as independent and uniformly distributed because it lacks this time-dependent structure.

Difficulties Faced in Time Series Forecasting

Forecasting in Time-series data is quite challenging because it includes several factors:-

  • The complexity of the data

Numerous trends, seasonality patterns, and other temporal structures can complicate time series data. It may be challenging to spot and model the underlying patterns in the data due to its intricacy.

  • Non-stationarity

Data from time series can be non-stationary, which means that the data’s statistical characteristics might change with time. This can make using established forecasting methods and models that depend on stationarity challenging.

  • Outliers

Time series data may contain outliers or other uncommon occurrences that substantially impact the forecasts. It might be challenging to spot and model these outliers, especially if they are uncommon or unexpected.

  • Missing data

Missing values in time series data might make using certain forecasting methods and models that call for entire data sets challenging.

  • Uncertainty

Making forecasts about uncertain future events is a part of time series forecasting. Due to this uncertainty, it may be challenging to assess how accurate the forecasts were, which may result in erroneous conclusions.

  • Limited historical data

Time series data can occasionally have scant historical data accessible, which makes it challenging to spot long-term trends and patterns in the data.

  • Model selection

Time series forecasting models and methods come in a wide variety, each having advantages and disadvantages. No one model works for all datasets; therefore, selecting the right one might be difficult.

To meet these obstacles, careful data pre-treatment, exploratory data analysis, and model choice are necessary. It also calls for comprehending the time series forecasting’s underlying mathematical and statistical principles and a readiness to modify and improve the models when new information becomes available.

Latest Application of Time Series Forecasting in the Real World

In many different disciplines, including finance, economics, engineering, social sciences, and environmental studies, time series forecasting has a wide range of applications. Here are a few typical instances of time series forecasting in use:

Latest Applications of Time Series in the Real World
  • Financial Forecasting: Future stock prices, exchange rates, and other financial factors are predicted using time series forecasting. Financial risk management also uses predicting the likelihood of financial crises and other occurrences.
  • Predicting future sales of goods and services is essential for production scheduling and inventory control. Time series forecasting is used to do this.
  • It is used to forecast energy demand for electricity, gas, and oil. This information is crucial for organizing and controlling the production and distribution of energy.
  • It is used in meteorology to forecast weather patterns, such as temperature, rainfall, and wind speed, which is crucial for organizing transportation, emergency management, and agricultural activities.
  • In epidemiology, time series forecasting is used to forecast disease outbreaks and their transmission, which is essential for public health planning and response.
  • Transportation planning and traffic management depend on the ability to predict traffic volume and congestion using time series forecasting.
  • It is used to anticipate environmental variables, such as air and water quality, which is crucial for managing natural resources and preserving public health.


These are only a handful of the numerous uses while doing the analysis of time series data. In general, it comes in handy whenever it’s necessary to extrapolate future values of a variable from past data.

In the next blog, we will talk specifically about the last difficulty, which is Model Selection. We will explore different models and methods for performing the same analysis.

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