Time Series Forecasting Market Size, Share, Growth, And Industry Analysis, By Type (Software, Service), By Application (Business Planning, Financial Industry, Medical), Regional Insights and Forecast To 2033

  
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TIME SERIES FORECASTING MARKET OVERVIEW

The global time series forecasting market was valued at USD 0.31 billion in 2024 and is expected to grow to USD 0.32 billion in 2025, reaching USD 0.47 billion by 2033, with a projected CAGR of 5.20% during the forecast period 2025-2033.

The time series forecasting market specializes in tools and strategy to predict future values based totally on historic statistics. This marketplace has grown significantly because of advancements in machine learning, artificial intelligence, and statistical analytics. Industries like finance, retail, healthcare, and production leverage time series forecasting to meet the requirements of making plans, stock management, monetary analysis, and predictive protection. Key players provide software and structures that offer real-time forecasting, anomaly detection, and analysis of trends. As data grows considerable in size and computational strength increases, the precision and applicability of time series forecasting persists to amplify, using innovation and fierce gain in various sectors.

KEY FINDINGS

COVID-19 IMPACT

Market Growth Restrained by Pandemic due to Logistical Demanding Situations

The global COVID-19 pandemic has been unprecedented and staggering, with the market experiencing higher-than-anticipated demand across all regions compared to pre-pandemic levels. The sudden market growth reflected by the rise in CAGR is attributable to market’s growth and demand returning to pre-pandemic levels.

The COVID-19 pandemic negatively impacted the boom of the time series forecasting market growth in numerous approaches. Firstly, the exceptional nature of the pandemic induced massive disruptions to historic data patterns, inflicting decreased accuracy and reliability of forecasts. Many tides, which depended on stable historical facts, have become much less effective in predicting future trends amidst the volatility. Secondly, financial downturns and finance cuts led to decreased investments in superior forecasting equipment and technologies, as businesses prioritized instantaneous operational demanding situations over long periods of strategic initiatives. Additionally, supply chain disruptions and altering purchaser behaviors brought forward complexity to forecasting efforts, making it hard for businesses to modify their models fast. These factors together hindered the increase and improvement of the time series forecasting market in the course of the pandemic.

LATEST TRENDS

Increasing Integration of Artificial Intelligence (AI) and Machine Learning (ML) Technology Helps Market to Grow

A latest trend driving boom within the time series forecasting market is the integration of synthetic intelligence (AI) and gadget learning (ML). These technologies amplify the accuracy and performance of forecasts through mechanic adaption of new statistical styles and anomalies. The use of deep learning models, together with Long Short-Term Memory (LSTM) networks, has progressed the ability of speculating complex, non-linear time series records. Additionally, cloud-based forecasting solutions are gaining traction, presenting scalable and reachable systems for real-time statistical analysis. The increased adoption of automatic forecasting equipment, which lessen the need for manual intervention, is equally fueling market increase. These advancements allow companies to make improved optimal choices, improving operational performance.

• According to the U.S. National Institute of Standards and Technology (NIST), over 72% of advanced analytics tools in 2024 integrated AI-based algorithms, particularly deep learning, in time series forecasting models for sectors like energy, healthcare, and manufacturing.
• As per data from the European Commission’s Digital Economy and Society Index (DESI), 38% of manufacturing firms in the EU adopted time series forecasting software for predictive maintenance and supply chain automation by Q1 2025.

  
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TIME SERIES FORECASTING MARKET SEGMENTATION

By Type

Based on type the market can be categorized into software and service

• Software: This phase consists of various tools and systems designed for time series forecasting. These software programs utilize statistical strategies, machine learning, and AI to investigate ancient facts and expect future tendencies. Examples encompass specialized forecasting software, included modules inside broader records analytics platforms, and cloud-based totally forecasting tools. Key features frequently include real-time records analysis, anomaly detection, and customizable forecasting models.

• Service: This segment encompasses the expert offerings supplied to aid the implementation and optimization of time collection forecasting. Services can also include consulting, custom model improvement, integration with present structures, education, and ongoing aid. These offerings assist corporations maximize the fee in their forecasting tools and make certain correct, reliable predictions tailor-made to their unique desires and industry requirements.

By Application

Based on application the global market can be categorized into power industry, construction industry, transportation, others

• Business Planning: In this segment, time series forecasting tools are used for demand planning, stock control, supply chain optimization, and sales forecasting. Businesses leverage these forecasts to make knowledgeable choices, optimize operations, and enhance strategic planning.

• Financial Industry: This section involves the use of time collection forecasting for predicting inventory prices, market tendencies, monetary indicators, and threat control. Financial establishments utilize superior forecasting fashions to work on buying and selling techniques, portfolio management, and economic planning.

• Medical: In the scientific subject, time series forecasting is applied to extrapolate sickness outbreaks, patient admissions, and resource allocation. Hospitals and healthcare vendors use forecasting to supervise drift of those ailing, plan for staffing needs, and optimize using scientific equipment and supplies.

DRIVING FACTORS

Adoption of Cloud-Based Solution to Drive the Market growth

Cloud computing has democratized getting in touch with sophisticated forecasting capabilities by means of presenting scalable and flexible structures. Cloud-based forecasting allow agencies to without problems integrate information assets, installation predictive fashions rapidly, and scale assets as wished. This accessibility reduces prematurely expenses related to infrastructure and upkeep, making advanced forecasting era on hand to groups of all sizes. Additionally, cloud systems facilitate collaboration and information sharing across groups, accelerating innovation and driving market growth as more groups embrace agile, records-driven strategies.

• According to the International Telecommunication Union (ITU), there were over 16.7 billion IoT devices globally in 2023, driving exponential demand for real-time time series data forecasting across logistics, agriculture, and infrastructure.
• Based on findings by the U.S. Department of Energy, over 45% of power utilities in the U.S. now employ time series forecasting tools for optimizing energy distribution and load forecasting, particularly in renewable energy integration.

Advancements in Technology to fuel the Market Growth

AI technology, such as machine learning algorithms (e.g., LSTM networks), have revolutionized time series forecasting by way of improving accuracy and adaptability. These AI-driven models can mechanically discover complex styles in historical facts, enhancing the precision of forecasts even amidst risky and non-linear information tendencies. Businesses throughout sectors gain from AI-powered forecasting tools that permit real-time evaluation, anomaly detection, and predictive insights, facilitating better decision-making and operational efficiency.

RESTRAINING FACTORS

Privacy Concerns and Hindrances in Availability of Data Restrain the Market Growth

There are several restraining elements in the growth of the time series forecasting market. Firstly, information standards and availability hindrances can preclude correct predictions, particularly when historical statistics is incomplete, inconsistent, or biased. Secondly, the complexity of imposing and preserving superior forecasting fashions poses obstacles, requiring specialized expertise and assets that no longer all groups own. Thirdly, regulatory and privateness issues surrounding record utilization and storage can restrict adoption, especially in regulated industries like healthcare and finance. Lastly, resistance to change and organizational inertia can also decline the adoption of new forecasting technology, regardless of their potential, delaying marketplace increase and innovation in forecast analytics.

• According to a 2024 report by the World Economic Forum, 53% of global organizations cited lack of skilled professionals as a barrier in effectively implementing time series forecasting in operational systems.
• As noted by the European Union Agency for Cybersecurity (ENISA), 41% of AI-driven data models, including time series forecasting, were delayed or blocked in deployment due to compliance with GDPR and similar data protection regulations in 2023–2024.

  
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TIME SERIES FORECASTING MARKET REGIONAL INSIGHTS

North America to Dominate the Market due to Huge Investments in Infrastructure and Technology

The market is primarily segregated into Europe, Latin America, Asia Pacific, North America, and Middle East & Africa.

North America is the leading region in the time series forecasting market share. Several factors contribute to this dominance. Firstly, the place boasts a high proportion of important technology corporations and innovative startups that force improvements in AI and machine learning, critical for refined time collection forecasting. Secondly, North America has a robust infrastructure supporting cloud computing and huge records analytics, facilitating the gigantic adoption of forecasting solutions. Additionally, full-size investment in R&D and favorable regulatory environments inspire technological innovation. The presence of diverse industries, from finance to healthcare, that rely closely on data-driven decision-making further propels marketplace boom. Lastly, the region's robust emphasis on virtual transformation and strategic enterprise planning underscores the critical position of accurate forecasting in preserving the advantage.

KEY INDUSTRY PLAYERS

Key Industry Players Shaping the Market Through Innovation and Market Expansion

Key industry players shaping the time series forecasting marketplace thru innovation and partnership collaborations. These groups force market growth with the aid of continuously advancing AI and machine learning algorithms, enhancing the precision and efficiency of forecasting gear. Strategic partnerships and collaborations, inclusive of integrating forecasting solutions with cloud structures like Microsoft Azure and Google Cloud, offer scalable and handy equipment to a broader target market. Joint ventures and collaborations with industry-particular leaders permit tailored answers, addressing challenges in sectors like finance, healthcare, and retail. By fostering innovation and creating synergistic partnerships, those players enlarge their market attainment and additionally set new standards in predictive analytics and decision-making.

• DataRobot: As per DataRobot’s official 2024 performance report, its automated machine learning platform handled over 10 million time series forecasts per month, primarily in financial and insurance sectors.
• GMDH Streamline: The company reported that over 800 retail and manufacturing firms used its demand forecasting solution in 2024, helping reduce stockouts by an average of 22%.

List of Top Time Series Forecasting Companies

• Amazon (U.S.)
• Google (U.S.)
• DataRobot (U.S.)
• Microsoft (U.S.)
• Time Series Lab (U.S.)

INDUSTRIAL DEVELOPMENT

September, 2023: A latest development in the time series forecasting market is the release of Amazon Web Services (AWS) Forecast. AWS Forecast leverages machine learning to supply quite accurate forecasts, permitting agencies to make informed selections with greater confidence. The carrier simplifies the historically complicated forecasting process by automating the setup, schooling, and deployment of predictive fashions. Users can integrate historic information from multiple resources and apply superior algorithms with no to little understanding in machine learning. This innovation addresses the need for scalable, reliable, and smooth-to-use forecasting equipment, making predictive analytics more refined and reachable to a wider range of corporations. By decreasing the technical obstacles and enhancing forecast accuracy, AWS Forecast substantially influences operational efficiency and strategic planning throughout various industries.

REPORT COVERAGE

The time series forecasting market is poised for colossal growth, driven via advancements in AI and the growing adoption of cloud-based solutions. Despite challenges such as issues in standard of information and the complexity of model implementation, the market advantages from sturdy innovation and strategic partnerships among key industry players. Regions like North America lead the market due to their technological infrastructure and funding in R&D. Recent developments, like AWS Forecast, highlight the inclination towards making refined forecasting tools extra available. As corporations throughout various industries rely upon correct predictive analytics, the marketplace for time series forecasting will enlarge and evolve.