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What Is Time Series Modeling? Forecasting Process and Model

If you are new to Time series and want a basic understanding of what is Time series? What kind of problem can you solve using this method? What does Time series data look like? and what are the methods available for Time-series forecasting etc. Then this blog can really help you to understand the basic concept of Time series. Once you understand the basic concept then it will help you to address business problems that you can solve using this method.

What is Time series modeling?

‘Time’ plays an important role when it comes to success in business. We always think running with time is very difficult and when it comes to running ahead of time: one word comes in our mind impossible, but technology has developed some powerful methods which make our business to ‘look thinks’ ahead of time.

I am talking about the methods of prediction and forecasting, methods which deal with time-dependent data is known as Time series modeling.  As the name suggests, data-dependent on the series of times where time refers to the year, month, quarter, day, hour, minute, etc. Below picture tells us how Time series data looks like:

What is Time series modeling?

In today’s world, most industries like Automobile, E-commerce, Stock exchange, Pharma, etc. are using Time series to make their business more profitable. Below are a few examples, how these sectors are making business profitable using this method:

Automobile companies are using this method to avoid the unbalanced production by forecasting the future demand of vehicles using the previous month/year’s sales data, which leads them to manage their inventory based on demand in the market and helps them to manage their budget for upcoming months/years with cost-efficient production.

 It’s a trend these days that E-commerce companies roll out many sales offer on their online platform (like Big billion sales, festival sales, Mega day offers, etc.) and here they are using Time series modeling to predict their web traffic, sales during this period which helps them to manage their web traffic and by doing this they can prevent their website from getting crashed or slowing down their web server because huge number of customers would be hitting the website during this time period rather than in normal days.  

In the Pharma domain, Time series modeling is used to predict the progression of the disease, assess time-dependent risk, mortality rate. Which helps a doctor to choose proper prescription based on the disease progress and risk factor. Time series also helps hospitals to manage their patient waiting lists, helps to predict consumption and sales of drugs, etc.

The stock market is a market where we buy and sell shares every second in a day which involves huge risk to the money of investors, where time series modeling helps to minimize the risk and maximize the profit by predicting the stock trends in an effective manner.

Why forecasting?

In Business, we always focus on how our growth will be in the future, what will be the future hold in the market, how much sales we will be going to get in upcoming days? to answer these questions we need forecasting because the results of these are dependent on time factor and decision, an organization going to make today will help our business outcomes in the future.

Read: How To Write A Resume Of An Entry Level Data Scientist?

Why forecasting?

Forecasting is a technique or projection of data that uses historical data as inputs to make informed estimates that help us to predict the direction of future trends of sales, demand, market size, weather, etc. based on the past and current actual data points. The business utilizes forecasting to determine their expenses for their future and which helps to plan budgets for every operation within their organization, budgets for the marketing/Digital marketing, budgets for acquisition/retention, budget for a new product, etc.

There are two types of forecasting

Qualitative Forecasting: Qualitative method is based on the experience, judgment and subjective knowledge in the forecasting of individuals. If we don’t have numerical data in that case this method helps in making important decisions. One method for this approach is the “Delphi Method”. In this method, a group of experts develops a forecast and one expert could be a decision-maker. Then every member of the group is questioned individually about his estimates and later responses get forwarded to an independent party, who abstract these forecasts and supporting statements and send back to the expert. This process keeps going on until a general agreement is reached. It is an effective method for long-range forecasting.

Quantitative forecasting: This method totally relies on historical data of any organization. It is a statistical technique for making a future prediction that uses numerical data and past effects. These methods are more effective than qualitative forecasting and are very useful according to current market demand. Below are some effective quantitative forecasting methods which are widely used in current business forecasting:       

  1. Simple Moving Average
  2. Exponential Smoothing
  3. Autoregressive Integrated Moving Average
  4. Neural Network

Forecasting Process:

Forecasting is not a simple step task, it can be done in multiple steps by experts. To understand better about the steps followed by experts to implement forecasting in real-time, refer below steps:

Forecasting Process:

1). Identify the problem: This seems simple at first but it’s tricky a bit. You cannot simply decide to run forecasting models on time-dependent data and there is no tool that is going to help you to decide.

You must spend some time with people who are responsible for maintaining the database and collecting data. Also, spend some time to answer questions like who the forecast is directed too, what is your consumer base, is it going to ease our business in future, is it going to help in cost-cutting, etc.

2). Collect Information: Collecting information here doesn’t mean data only, it also means collecting business knowledge from experts and knowledge about data (means what data points are saying about). There might be a case where we may not have data then we will be dealing in qualitative forecasting and we will totally depend on information shared by experts.

Collecting data also refers to having actual information that is needed for forecasting and no extra information because extra info can lead your forecasting somewhere else.

Read: How to import Data into R using Excel, CSV, Text and XML

3). Preliminary Analysis: Pre-analysis is necessary for any model-building task because it tells you whether this data is good enough to perform actual analysis or not. In the case of forecasting, this analysis will tell you about trends, seasonality, patterns in the data which will help you in building the Time-series model.

Also, this step can help you in handling tasks like missing value treatment, outlier treatment, removing extra information and making some educated assumptions.

4). Choose the Forecasting Model: Once information is collected and treated, we must come up with one Forecasting model which according to you will give the best possible predictions. There is no single model that performs well in every situation, it depends on the nature of your problem and data availability.

In case if we don’t have any historical data then we must use qualitative forecasting. There are two famous qualitative forecasting methods:

  1. Market Research
  2. The Delphi Method

But if there is historical data available then there are many mathematical methods that are available for forecasting including regression models, Box-Jenkins ARIMA Model, Exponential Smoothing Models, etc.

5). Data Analysis: Once you choose your model there comes to perform analysis using that method. We will pass the data by keeping all the assumptions in mind for that method and perform analysis.

6). Verify Model Performance: This step is important because here we must evaluate the performance of each step along with model performance.

After validating all the steps, we do check the accuracy of our model prediction by comparing it with our actual results. If all seems good, then it is good to go-ahead for production otherwise we must investigate every step to make our model accuracy better.

a). Goals: Whenever we perform any task we make some goals that we can achieve by performing that task. Here also we have two goals:

  • Descriptive goals can be achieved by performing a preliminary analysis. Aim of this goal is to understand our data carefully and yield some useful information like what was happened in the past and why? The behavior of data, in the case of forecasting what is trend, seasonality, pattern in data and can prepare data for further analysis.
  • Predictive goals can be achieved by performing further analysis after data preparation and descriptive analysis. In this case, predictive goals are like how our model is performing, what is the accuracy of the model, If the model is good then for how long in the future it can make better possible predictions and how it can affect our business in a positive way.

Forecasting Horizon and updating:

The forecasting horizon is the length in the future for which we want our model to make predictions. It can vary from short-term forecasting horizon to long-term forecasting horizon.

Read: What Is Data Science? A Beginners Guide To Data Scientists

It also depends on the nature of forecasting or can say depends on the business problem for e.g. If we are doing forecasting for stock exchange, then this horizon will be very short like 1 hour or 4 hours or 12 hours, etc. but if we are predicting sales then it will be 1 month or quarter or half-year or yearly etc.

Forecasting horizon is mainly divided into three parts:

  • Short-range forecast: Range for this is typically less than 3 months but has a time span of up to 1 year and mostly used planning for workforce levels, job assignments, product level, etc.
  • Medium range Forecast: This has a range of 3 months to 1 year but has a time span of 1 to 3 years and widely used for production planning, budgeting, sales planning, etc.
  • Long-range Forecast: This has a time span of more than 3 years and widely used for Research and development, Planning and installing new plants, etc. can say mostly for long term projects. 


Updating your forecasting model is also necessary because one model will not work for a whole life. Now the question is how and when we must update our model.

We must monitor our model performance continuously whenever we are making a prediction using that model and check all parameters which are necessary for a better model and if you found that model performance is not so good and data on which we build model is now changed then we must update our model.


I hope this article helped you to understand the basic concept of Time series and if so then you are ready to go to the next level. If you have any questions about Time series then you are free to ask and we are happy to answer and also let me know what you gained from this article and any new idea is most welcome.

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