An illustration titled 'Time Series Forecasting' shows a business analyst seated and working with a laptop. There is a sand clock beside him.

Time Series Forecasting! How Does It Improve Business Decision-Making?

By Nivin Biswas Category Hot Topics Reading time 8-9 mins Published on Jan 23, 2023

Time Series Forecasting- The Way to Achieve Seamless Experience for Your Business Growth

Have you ever booked cabs using different ride-hailing apps like Ola and Uber during pick-hours?

If so, you must know the pain of 'Sorry, no nearby ride found,' 'ride canceled by the driver,' and other similar messages shown by the app. Such occurrences lead to gradual customer churn. However, such a problem now can be taken under control with the proper implementation of time-series forecasting.

What is the time-series method of forecasting?

Time series forecasting method is a technique for determining forecasts that could be used to anticipate the future. You can do it by examining historical trends as well as live trends and determining which model works best to forecast the current time series.

Time series forecasting uses statistics and modeling to examine time-series data to produce forecasts and guide strategic decision-making scenarios. Hence, businesses can easily make informed decisions.

For example, in the case of the ride-hailing app, it will look for the data like:-

  • At what time the demand rises,

  • In which location do users face most of the difficulties, etc.?

An illustration of time series analysis shows a person standing in front of a table and using a desktop. There is a wall clock behind him.

The basic concept of time-series analysis:-

What exactly is time-series data?

Making smart selections necessitates knowing how to examine and comprehend this data. One kind of data that has the potential to reveal a lot of information is time-series data. Cross-sectional data showing a variable's values over a period of time is known as time-series data.

Several methods are used depending on the time series data size, but the fundamental concept is the same:

"forecast future data using existing data."

Time series data is not always a precise forecasting method, and the likelihood of forecasts might vary a lot. Especially when dealing with a variable that swings regularly. It's virtually a sloppy procedure.

A time series can be divided into four parts, each of which expresses a different aspect of the movement of the time series values.

These four elements are:-

  • The word "secular trend" characterizes the movement and the term.

  • 'Seasonal variations' represent seasonal changes.

  • 'Periodical but not seasonal fluctuations' are referred to as cyclical fluctuations.

  • 'Irregular variations' are another non-random source of series variations.

How does the forecasting method work?

Forecasting is an integral aspect of strategic planning and every bit of business decision-making. It can be used for various purposes, including assessing long-term corporate operations and analyzing short-term price changes, customer targeting, or personalized product/service recommendation. Forecasting needs tricky and precise data analysis techniques applications.

Investors use forecasting to determine what a firm will encounter in the future and whether occurrences will influence the company's growth. Hence, forecasting has become the most critical metric for business statistical analysis. There are four different methods for forecasting. Examples of these types include:-

  • what-if forecasting

  • what-would-be forecasting

  • what is forecast forecasting

  • what will be forecast forecasting

The temporal ordering of time series data is natural. This distinguishes time series forecasting from cross-sectional data studies, where the observations are not always analyzed in a particular order.

For example, when comparing people's incomes to their respective educational levels, there are various ways that the data on the individuals might be organized.

The following are some of the peculiarities - traits or qualities of forecasting:-

  • The emphasis of forecasting is on what will occur in the future.

  • It demonstrates the likelihood that an occurrence will take place.

  • It examines both historical and current data.

  • It employs statistical analysis techniques and technologies.

  • It is based on personal experiences.

Procedure for prediction:-

  1. Pick a model.

  2. Divide the data into sets for training and testing.

  3. Attach the model to the train set.

  4. To assess the model, use test set.

  5. Refit the model to the complete dataset and forecast unknown data in the future.

Here are several methods by which forecasting is accomplished.

I. Data analysis:-

Forecasting is crucial for the corporate sector. Without proper data analysis, it would be challenging for a company to have any direction as it becomes harder to specify objectives and strategies.

Businesses could only make decisions by depending on analyzed past, present data, and future insights. It is a difficult task, as it requires a lot of time and expertise to integrate into a financial firm or industry- mainly when it comes to the matter of present (live) data.

II. Collection of data:-

Data collection is a lengthy procedure with time taking complexity. Many folks are concerned about it because they are unsure where to begin.

Today, however, we have several tools that assist us in data collection, making it easier. It is crucial to clearly understand the 'Data you are collecting' and 'the purpose for which the data will be used'. That is why we must concentrate on which data is significant and which is not.

III. Trend analysis:-

Future occurrences are projected using trend analysis. Predictions become more accurate and precise when patterns are researched in specific time frames that foresee the future. To forecast future events, one must first examine tendencies before forecasting them. The trend analysis process isn't capable of making long-term predictions. Despite this, trend analysis is used to predict the frequency of events over a specific period.

IV. Result comparison:-

It is generally acknowledged that a thorough evaluation is necessary before comparing estimated results. The actual consequences, however, could occasionally diverge from those anticipated outcomes. This is acceptable, but if the actual findings dramatically deviate from the anticipated outcomes, it becomes vital to determine why the results are not accepted.

V. Prediction-based Precautions:-

The forecasting process can be improved and developed continuously based on prior experience. With future predictions, the weaknesses of a firm can be healed. Therefore, it becomes very important to regularly provide feedback on past projections.

Furthermore, a forecast can be enhanced by approaching it from a different angle, using a different method, and using a different tool.

Therefore, there is a need for comments following the forecast creation. The comments main objective is to enhance and perfect the prognosis for the future.

There are various types of time-series models. The following image shows the list of the most widely used types.

An illustration lists the different types of time series models as follows:
Autoregressive (AR) model
Moving average (MA) model
Autoregressive moving average (ARMA) model
Autoregressive integrated moving average(ARIMA) model
Autoregressive integrated average(ARIMA) model
Vector autoregressive (VAR) model
Vector error correction(VECM) model

Different types of time-series models.

  • Autoregressive (AR) model
  • Moving average (MA) model
  • Autoregressive moving average (ARMA) model
  • Autoregressive integrated moving average (ARIMA) model
  • Seasonal autoregressive integrated moving average (SARIMA) model
  • Vector autoregressive (VAR) model
  • Vector error correction (VECM) model

Importance of forecasting:-

The following points are relevant to the merits of Forecasting:-

  • Forecasting provides accurate and pertinent information about recent and ongoing events that are anticipated to occur in the future. Well-resourced data and effective planning are necessary for this.

  • It offers managers and leaders the confidence they need to make vital decisions.

  • It keeps managers engaged and aware to meet the difficulties of future events and environmental changes.

Here is an example of a time-series modeling that will surely help you to get a better understanding:-

Moresimple_prediction = combined_data_train.
groupby(['Dept']), as_index=False). agg({'weekly_Sales':
Print (moresimple_prediction. shape)
Moresimple_prediction. columns = {"_". join(X) for X in moresimple_prediction. column.ravel ()]
Print (Moresimple_prediction. columns['weekly_Sales_count]. describe ())
Moresimple_prediction =
moresimple_prediction-drop(['weekly_Sales_count'], axis =1)
Moresimple_prediction.columns = ['Dept', 'Sales_prediction]
Moresimple_prediction['prediction_typre'] = 2
Moresimple_prediction['prediction_type] = 2
Moresimple_prediction. head()


An chart shows an output-generated model for sales prediction where it is divided into three sub categories, Dept, Sales_Predcition, Prediction_type.


The above example shows the type of analysis concerning the departments. Utilizing previous data, predictive analytics forecasts future events. Typically, a mathematical model that captures significant trends is constructed using historical data. In this example, I have discovered the expected department sales concerning old Data.

Summing up:-

You can use time series forecasting to assist you in making tactical and strategic decisions. Using the ideas from this article, you may use time series forecasting to learn more about how various variables change over time and predictions.

If you want to build an effective time-series model for solving current business problems, you can enroll in an industry-ready artificial intelligence course. In case of amateur learning about time-series advancement, you can follow us on Facebook, Youtube, Linkedin, and Twitter.