Time Series Forecasting

Time Series Forecasting

Get any data at continuously different time intervals and make the predictions you need, and Amazinumu’s team will help you with this.

Time Series Forecasting

What is Time Series Forecasting?

Time series forecasting involves the process of collecting historical data, analyzing it, using statistics and modeling for forecasting, and predicting extreme values. This kind of forecasting doesn’t give you a 100 percent prediction, but it does give you an idea of what outcomes are more or less likely. In this case, the more accurate data our specialists have, the more accurate the forecasts will be. Forecasting is often used with time series analysis. In this case, Time Series Analysis involves developing models to understand the data, and Forecasting analyzes the subsequent role of this data with predictable extrapolations of future events.

How does it Work?

When forecasting, it is important to understand your goal. To do this, you can answer 2 questions:

  1. Volume of your data. More data provides more opportunities for accurate application and model training, which in turn leads to better and more accurate results.
  2. Time interval. Short time intervals make it possible to form more reliable forecasts.

Time series forecasting considerations

Time series forecasting considerations

Types of Time Series Forecasting

There are generally several types of time series forecasting. The use depends directly on the data we will be dealing with. In general, the following are distinguished:

Univariate Forecast

Univariate Forecast includes only one variable that depends on time. An example of use cases for this type is tracking hourly temperature values for a certain region and future forecasts. Thus, using historical data to track temperatures is one-factor time series forecasting.

Univariate Forecast

Multivariate Forecast

Multivariate Forecast consists of several variables. Each of the variables is not only dependent on its past values, but also has some dependence on other variables. Such dependence is useful for the formation of future values.

Multivariate forecasting involves the use of one of two types of variables:

  • Exogenous – input variables on which the output variable depends, but which are not influenced by other variables.
  • Endogenous are also input variables that are influenced by other variables and on which the output variable depends.
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Business Value

  • Identification of trends and threats
    With Time Series Forecasting, you can forecast trends in your profits and prevent losses. Our Data Scientists will do everything possible to help you get the information that will help your business avoid unnecessary costs, while directing resources in the right direction. Also, on the basis of data from time series, you can set reasonable goals, accurately draw up a budget and make balanced and considered decisions.
  • Understanding the driving force
    Time series will also help you understand the underlying components and factors affecting your business. This will give you the opportunity to better understand the cause of all processes and phenomena that affect the vital activity of your business.
  • Hypothesis testing
    With autocorrelation and cross-correlation, our Data Scientists will provide you with information about the relationships between observations in time series data. Thus, thanks to regression modeling, you will be able to test hypotheses without risk to the business.
  • Detection of anomalies
    Time series monitoring will give you access to important data such as Changed Behavior, Level Shifts, Outliers, and Anomalous Patterns. This will allow you to make informed decisions and notice any minor changes that may negatively affect your business.

Use cases of Time Series Forecasting

E-commerce and retail

Marketers frequently use data to gauge the success of their initiatives, but data by itself frequently falls short of painting a whole picture. Marketers can better understand what drives sales outside of a campaign by accounting for seasonality and other trends. When seasonality is the true cause of poor sales, no one wants to blame a failed marketing campaign for it. Forecasting demand and/or trends: Using your historical sales data, time series analytics can assist you in projecting future sales performance. This will assist you in organizing your budget, personnel, promotions, and inventory for the entire year. You can optimize your marketing and customer support resources for peak activity if you can identify patterns in customer behavior, such as purchasing habits or website usage.

Logistic and Delivery

Time series analytics allows you to examine trends in traffic flow and congestion over time and pinpoint the variables that affect them. This includes traffic pattern analysis. This is a crucial method for engineers and planners of public transportation systems to create more effective roadways and networks.

Optimizing the schedule: Transportation companies need to know when their peak days, hours, and seasons are, just like retail establishments, to supply the demand (in this case, by adding more routes during those times)


By examining trends in maintenance requirements and equipment downtime, manufacturers can better plan their maintenance and downtime schedules. To help manufacturers improve the efficiency of their processes, you can also analyze production patterns to find bottlenecks and inefficiencies.


Knowing when to buy and sell stocks depends on the ability to forecast stock prices using historical data. Whether a particular stock is a buy, sell, or hold, as well as for how long, depends on a number of factors, including cycles, macrotrends, long- and short-term trends, and macrotrends. You can evaluate the risk involved with different investments by using the same techniques to evaluate stock price volatility. You can optimize the mix of assets in an investment portfolio to increase your chances of maximizing return and lowering risk by looking for trends in the returns of various asset classes, such as stocks, bonds, and commodities.


You can find the variables that could affect a student’s performance over time by using time series analytics. Knowing this is essential for early intervention because it allows you to assist students as soon as they exhibit warning signs for a known pattern or cycle. Educational institutions can forecast and plan for future enrollment by utilizing their historical enrollment data. Time series forecasts can also assist you in keeping track of trends in education, such as the proportion of students pursuing a specific subject or preferring a specific type of learning environment (in-person, remote, asynchronous, etc.). Schools can then ensure that they are offering the courses and programs that students are interested in.

Weather conditions and environment

Because they rely on past data to generate their predictions, weather forecasts are not always accurate. Their predictions will be more accurate the closer they are to the relevant data and the more data they have overall. To schedule our beach outings, the majority of us consult the weekly or daily forecasts. Environmentalists can map climate change and pinpoint related trends and cycles by examining historical patterns in temperature and other climate data. Since natural disasters, such as hurricanes and earthquakes, also follow cycles, you can utilize historical data to predict when the next one is likely to happen and make appropriate preparations.

Companies that already use Time Series Forecasting

Procter & Gamble (P&G)

P&G optimizes product distribution and production by using time series forecasting for demand planning in the consumer goods sector.

Cisco Meraki

For network performance analysis, traffic pattern prediction, and networking hardware deployment optimization, Cisco Meraki uses time series forecasting.


To predict sales, estimate revenue, and assist companies in making decisions, Salesforce integrates time series forecasting into its CRM platform.

Johnson & Johnson

For demand planning, supply chain optimization, and inventory management, Johnson & Johnson uses time series forecasting in the pharmaceutical and healthcare sectors.v


Time series forecasting is used by Intel to optimize computer chip production schedules through demand planning in the semiconductor industry.

Delta Electronics

Time series forecasting is used by Delta Electronics to forecast how much energy electronic devices will consume, optimize power supply options, and increase energy efficiency.

Siemens Healthineers

Time series forecasting is used in healthcare by Siemens Healthineers to forecast patient admission rates, optimize resource allocation, and boost hospital productivity.

Delta Airlines

Time series forecasting is used by Delta Airlines to manage crew assignments, optimize flight schedules, and anticipate passenger demand.

GE (General Electric)

Time series forecasting is a tool used by General Electric to minimize downtime, maximize industrial machinery performance, and anticipate equipment maintenance needs.

Johnson Controls

Time series forecasting is a tool used by Johnson Controls in building automation to forecast demand for heating and cooling, optimize energy use, and raise overall building efficiency.


Amazinum Portfolio

Check out a few of our recent projects
Navigating the Future: Harnessing AI and ML in the Maritime Industry
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Smart Eyes: The Integration of AI and ML in Surveillance Cameras for Enhanced Security

Beyond Monitoring: The Power of AI and ML in Construction Site Surveillance

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Our industry knowledge and background give our clients and partners confidence that we understand their business. Here we highlighted a few top industries we are good at, penetrating to the smallest details and nuances of a certain branch.

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Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

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Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together

Contact Us

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Bazarnitska Alina

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Stopnyk Zoriana

Client Partner

Vitaliy Fedorovych contact us photo

Vitaliy Fedorovych

CEO, Data Scientist

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

Hello there!

Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together

Contact Us

Click or drag a file to this area to upload.

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