Time Series Forecasting for Smarter Business Decisions

Predict future trends with time series forecasting tailored to your business. We help you make data-driven decisions, plan ahead with confidence, and spot changes before they impact your bottom line.

Discuss your forecasting needs

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    Software Service providers

  • ISO 27001 certification
    by Bureau Veritas

  • GDPR-Compliant processes
    for responsible data protection

  • AWS trusted infrastructure
    for scalable solutions

  • Bureau Veritas —
    an independent global leader in testing, inspection, and certification.

Why Is Time Series Analysis Forecasting a Competitive Advantage?

Unpredictable markets, shifting customer behavior, and complex supply chains make planning harder than ever. Time series analysis forecasting helps bring more clarity to planning. By learning from historical data, forecasting can help you anticipate change, reduce waste, and make more proactive decisions.

  • Optimized Inventory and Supply Planning

    Running out of stock and overstocking both hurt profit and reputation. Accurate forecasting helps balance inventory levels, reduce storage costs, and keep the right products available when and where they are needed.

  • Smarter Financial Planning

    Fluctuating revenue and uncertain cash flow can derail long-term plans. Predictive modeling can help you anticipate sales cycles and plan budgets on a stronger data foundation.

  • Improved Operational Efficiency

    If you rely on guesswork, inefficiencies can multiply. Time series forecasting models reveal usage patterns and performance trends, helping you streamline processes and reduce unnecessary costs.

  • Data-Driven Strategic Decisions

    Relying on intuition alone can limit growth opportunities. Time series analysis and forecasting turn data into foresight, giving teams a stronger basis for investment and expansion decisions.

  • Turn uncertainty into data-driven clarity

What Our Time Series Forecasting Services Can Cover

We provide custom time series forecasting services to help businesses make data-driven decisions with confidence. From demand prediction to risk modeling, our teams design and implement forecasting solutions that align with your business goals.

  • Forecasting Model Development

    Engage data scientists and ML engineers to design and train forecasting models tailored to your datasets, goals, and technical stack. Where suitable, we can also explore generative AI for time series forecasting, for example to generate synthetic time-series data or support scenario modeling.

  • Predictive Maintenance and Operations Planning

    By applying time series data analysis to equipment and performance data, we help organizations anticipate maintenance needs and reduce unplanned downtime. This capability builds on our expertise in predictive analytics services, supporting operational continuity, risk management, and smarter resource allocation.

  • Anomaly Detection and Trend Analysis

    Our specialists develop models that identify unusual behavior and emerging patterns in your data, such as demand spikes, performance drops, or fraud indicators. Earlier visibility helps teams investigate issues sooner and respond before they become more disruptive

  • Data Preparation and Feature Engineering

    We help teams make their time-based data ready for forecasting. That means cleaning it up, filling gaps, handling seasonality, and creating features that capture what really drives performance. The result is data that’s easier to work with and models that deliver more accurate, understandable results.

  • Forecasting System Integration

    Our engineers support the integration of forecasting outputs into your existing infrastructure, such as ERP, CRM, or BI tools. Predictive insights become available within the systems teams already use for day-to-day decisions.

  • Financial and Revenue Forecasting

    We help finance and strategy teams predict revenue, cash flow, and expenses with greater accuracy. Our models make it easier to plan budgets, spot financial risks early, and make confident long-term decisions backed by data.

  • Demand and Sales Forecasting

    We build forecasting solutions that help you understand market trends, predict customer demand, and manage inventory more efficiently. These insights support demand planning, pricing, purchasing, and production decisions, helping reduce the risk of overstocking or delays.

  • Custom AI Workshops for Teams

    We offer custom tech training workshops led by senior AI engineers to help your team strengthen forecasting capabilities. These collaborative sessions focus on solving real business challenges, improving data workflows, and expanding internal expertise.

  • Bring clarity to your data and plan with confidence

Flexible Cooperation Models to Fit Your Goals

Every company has different needs. Whether you’re scaling your team, launching a defined project, or exploring new AI capabilities, we adapt our approach to match your timeline, budget, and priorities.

  • Dedicated Development Teams

    Direct communication and control

    Get a dedicated team of data scientists, ML engineers, and analysts who work closely with your internal team. We match specialists to your tech stack, data setup, and roadmap, with support ranging from model development to deployment. You stay in charge of direction and priorities, while Beetroot handles team setup, onboarding, and delivery continuity.

  • Project-Based Solutions

    End-to-end support

    If your goals or timelines are clearly defined, our project-based approach delivers a complete time series analysis or forecasting solution from start to finish. We plan and manage the work from data preparation and model validation to implementation, with agreed milestones, validation criteria, and handover.

  • Custom AI Workshops

    Hands-on team training

    Collaborate with senior AI and data specialists through hands-on sessions tailored to your current challenges. Workshops can help your team strengthen forecasting skills, review model performance, and explore relevant AI techniques using your tech stack and real business cases.

Choose a cooperation model that fits your team, project scope, and current stage

Example Tools and Technologies We Use

Our teams use established tools and frameworks to build forecasting systems around your data, infrastructure, and project requirements. The technology stack can cover data processing and modeling as well as deployment and monitoring, depending on the scope.

  • Data Preparation and Processing

    • Python
    • Pandas
    • NumPy
    • Apache Spark

  • Statistical Forecasting

    • statsmodels
    • Prophet

  • Machine Learning and Deep Learning

    • scikit-learn
    • XGBoost
    • TensorFlow
    • PyTorch

  • Data Storage and Warehousing

    • PostgreSQL
    • BigQuery
    • Snowflake

  • MLOps and Deployment

    • MLflow
    • DVC
    • Docker
    • Kubernetes

  • Cloud and Model Monitoring

    • AWS
    • Microsoft Azure
    • Google Cloud
    • Prometheus
    • Grafana
    • Evidently AI

Meet Your Time Series Forecasting Team

Connect with data scientists, ML engineers, and analysts who can support your forecasting project. Their work can span predictive modeling and model validation, as well as interpreting results so your team can use them in day-to-day decisions.

  • $48/h

    Machine Learning Engineer (Mid-level)

    Alex F., 4+ years of experience
    Alex has worked on projects ranging from customer segmentation to demand forecasting. He builds and refines ML models using Python, TensorFlow, and scikit-learn. He’s strong in data preprocessing and feature engineering and is comfortable deploying models in production using Docker and AWS.
    • Apache Kafka / AWS Kinesis / Airflow / AWS Glue
    • Keras / TensorFlow / PyTorch
    • NumPy
    • Orchestration: Kubernetes, Docker
    • Pandas
    • Python
    • Scikit-learn / Statsmodels
    • SQL (query optimization, window functions)

    Request full CV

  • $42/h

    Middle ML Engineer

    Daniel M., 3+ years of experience
    Experienced with crafting end‑to‑end CNN pipelines in Python, leveraging PyTorch / TensorFlow and frameworks such as YOLO, RetinaFace, and SSD to deliver fast, accurate object‑ and face‑detection models.
    • CUDA / ONNX / TensorRT
    • Keras / TensorFlow / PyTorch
    • Matplotlib
    • NumPy
    • OpenCV
    • Python
    • RetinaFace
    • scikit‑image
    • SciPy
    • SSD (Single Shot Detectors)
    • Torchvision
    • YOLO

    Request full CV

  • $85/h

    Senior Data Scientist

    Magdalena R., 10+ years of experience
    A highly experienced data scientist with a proven track record of leading complex data science projects from inception to deployment. Expertise in developing and implementing advanced ML models, conducting statistical analysis, and providing actionable insights to drive business decisions.
    • Apache Kafka / AWS Kinesis / Airflow / AWS Glue
    • Cloud Platforms: AWS, Azure, GCP
    • Keras / TensorFlow / PyTorch
    • Processing: Hadoop, Spark, PySpark
    • Python
    • R
    • Scikit-learn / Statsmodels
    • SQL (query optimization, window functions)

    Request full CV

  • $95/hr

    AI Software Engineer (FDE) — embedded

    Roman V., 10+ years of experience
    Focus: Embedded ownership inside a single customer, production reliability for LLM systems, architecture under token/latency budgets, compliance fluency (EU AI Act, financial/healthcare), team enablement.
    • Agent Orchestration
    • Cloud Platforms: AWS, Azure, GCP
    • IaC/Config: Terraform, CloudFormation (IaC), Ansible
    • LLM System Design
    • LLMs
    • MLOps
    • Python
    • RAG
    • TypeScript

    Request full CV

  • $65/hr

    AI Engineer — Forward Deployed (FDE)

    Borys N., 5 years of experience
    Focus: Production AI deployment in customer environments, integration debugging, model configuration against real client data, post-PoC operationalization.
    • API Integration
    • LLMs
    • Orchestration: Kubernetes, Docker
    • Prompt Engineering
    • Python
    • RAG
    • SQL
    • TypeScript

    Request full CV

  • $82/hr

    Forward Deployed AI Engineer

    Vitalii K., 10+ years of experience
    Focus: Business-technology alignment, AI opportunity assessment, solution architecture, delivery strategy.
    • AI/ML Systems
    • Cloud Platforms: AWS, Azure, GCP
    • Data Engineering
    • LLMs
    • Python
    • RAG

    Request full CV

  • $65/h

    Data Architecture Engineer

    Laura S., 8+ years of experience
    Laura excels in building robust data models and architectures for real-time analytics and business intelligence. Her work ensures efficient data flow and storage, aligning with the needs of data-driven organizations.
    • MongoDB / Redis / DynamoDB / InfluxDB
    • PostgreSQL / MySQL / SQL (general) / Snowflake / Redshift
    • Processing: Hadoop, Spark, PySpark

    Request full CV

  • $22/h

    Data Engineer

    James N., 6+ years of experience
    Skilled in Kubernetes, AWS, GCP; experienced in managing production clusters across clouds.
    • Cloud Platforms: AWS, Azure, GCP

    Request full CV

  • $50/h

    Data Scientist & LLM Engineer

    Mohammed S., 5+ years of experience
    Mohammed translates theory into real-world success. He merges standard NLP with generative modeling to refine automation for client workflows.
    • Data Science
    • LLMs
    • NLP

    Request full CV

  • $55/h

    Senior Data Analyst

    Olha B., 8+ years of experience
    A highly experienced Senior Data Analyst with a track record of driving strategic business outcomes through advanced data analysis and modeling. Possesses deep expertise in statistical inference, predictive modeling, and data storytelling, effectively communicating complex findings to both technical and non-technical audiences.
    • BI tools (Power BI, Tableau, Looker Studio)
    • Cloud Platforms: AWS, Azure, GCP
    • Data governance
    • Data warehousing
    • NumPy
    • Pandas
    • Python
    • Scikit-learn / Statsmodels
    • SQL (query optimization, window functions)

    Request full CV

  • $34/h

    Application Security Engineer

    Den B., 4+ years of experience
    Skilled in global penetration testing, including web application, API testing, social engineering, OSINT, external network, and Active Directory assessments. Proficient in using methodologies like OWASP Top 10, OWASP API Top 10, WSTG, ASVS, PTES, and CASA to conduct thorough security assessments and identify vulnerabilities.
    • Cloud Platforms: AWS, Azure, GCP
    • DevOps
    • Java / Kotlin
    • JS (React / Angular / Vue)
    • PHP: PHP, Laravel, Symfony, API Platform
    • Python (Django/Flask/Fastapi)

    Request full CV

  • $38/h

    Senior Software Engineer

    Gabriela L., 10+ years of experience
    Skilled in multiple languages, Agile, leadership, with strong communication skills.
    • PHP: PHP, Laravel, Symfony, API Platform
    • Python (Django/Flask/Fastapi)

    Request full CV

Our Forecasting Implementation Process

Implementing time-series forecasting AI typically involves several stages, from assessing data readiness to validating and integrating the selected model. We adapt the process to your goals, data environment, and project scope.

  • Discovery & Data Collection

    Step 1

    We start by identifying your business objectives and reviewing relevant time-stamped data from internal and external sources. This stage includes assessing data quality, coverage, and granularity to determine whether the available data can support the intended forecast.

  • Data Preparation & Feature Engineering

    Step 2

    Our data scientists clean, transform, and enrich datasets to make them suitable for modeling. We address missing values and outliers, assess stationarity and seasonality, and engineer features that reflect relevant trends and business drivers.

  • Pattern Analysis & Model Selection

    Step 3

    We compare statistical, machine learning, and hybrid approaches based on your data and goals. Depending on the use case, this may include exponential smoothing, ARIMA, SARIMA, Prophet, XGBoost, or LSTM, followed by testing to identify the most suitable approach.

  • Model Validation & Optimization

    Step 4

    Our team evaluates model performance through backtesting, time-series cross-validation, and relevant error metrics such as RMSE or MAPE. We tune the selected approach while balancing forecast accuracy, interpretability, and computational cost.

  • Deployment & System Integration

    Step 5

    Once validated, the model can be deployed on-premises or in the cloud and integrated with systems such as ERP, CRM, or BI dashboards. Forecasts can then be made available within the tools and workflows your teams already use.

  • Monitoring & Retraining

    Step 6

    Where ongoing support is part of the scope, we configure monitoring for model drift, data-quality issues, and changes in forecast performance. Retraining can be scheduled or triggered by agreed thresholds, with validation before an updated model is released.

Forecasting for Data-Rich Industries

Forecasting supports decision-making in industries where timing matters. Multivariate time series forecasting can help organizations estimate demand, assess changing risks, and improve planning by combining historical data with multiple relevant variables. Our teams adapt the approach to each company’s data environment, systems, and goals.

  • FinTech

    In financial services, accuracy and timing are key. Forecasting models can help financial institutions estimate revenue and cash flow, monitor liquidity indicators, and identify changing transaction patterns. Our specialists can integrate forecasts into financial data pipelines to support financial modeling, risk monitoring, and anomaly detection.

  • Retail

    Retail businesses face constant shifts in consumer demand, seasonality, and supply chain dynamics. Time series forecasting helps identify purchasing patterns, support inventory management, and inform promotion planning. Our experts can connect data from POS, CRM, and logistics systems to support stocking and pricing decisions.

  • EnergyTech

    Forecasting can help energy companies estimate consumption, renewable energy output, and grid loads. These insights support production scheduling, capacity planning, and resource management, and may help reduce avoidable operational losses.

  • HealthTech

    Healthcare teams need timely insights to manage patient flow, staffing, and resources. We can help integrate forecast outputs into existing operational tools, supporting more coordinated capacity and service planning.

  • GreenTech

    Companies focused on sustainability use forecasting to analyze emissions patterns, estimate renewable energy output, and plan resource use. Where suitable, AI for time series forecasting can be combined with broader analytics to identify patterns in environmental data and support efficiency and reporting decisions.

  • Manufacturing

    Forecasting supports production planning, maintenance scheduling, and demand prediction. It can help manufacturers prepare for disruptions, coordinate capacity, and make operational decisions on a stronger data foundation.

Explore how forecasting could support planning in your industry

Why Build a Time Series Analysis Solution with Beetroot?

Forecasting projects work best when technical decisions stay connected to real business needs. Beetroot brings together AI, data, and software engineering expertise through cooperation models shaped around your team, systems, and project stage.

  • People-Centered Collaboration

    Clear communication and mutual trust are central to how we work. Depending on the setup, our specialists can collaborate closely with your internal team or support a defined project while keeping priorities, responsibilities, and decisions transparent.

  • Extensive AI and Data Expertise

    For time series forecasting machine learning projects, our specialists can support model development, validation, data pipelines, and system integration. We connect technical work with practical business understanding to create solutions that work in real life.

  • Flexible Team Engagement

    Whether you need a dedicated forecasting team, short-term specialists, or technical guidance to boost internal capacity, we help you scale with confidence. Our model combines in-house continuity with flexibility, so you can adjust team composition as your projects evolve.

  • Responsible Approach to AI

    We center responsible innovation in our work. Our teams consider privacy, data quality, bias, explainability, and computational efficiency where relevant to the use case. We can also help document design decisions and controls that support your internal governance and review processes.

  • Structured, Adaptable Process

    We follow a clear process that keeps projects organized and flexible, from data assessment and model validation to integration and handover. Regular check-ins help surface risks early and adjust the plan as new findings emerge.

  • Knowledge Sharing and Team Enablement

    Documentation, collaborative working practices, and custom workshops can support knowledge transfer when included in the scope. The goal is to provide your team with a clearer understanding of the models, data, and maintenance needs behind the solution.

Clients Say

Our clients work across a wide range of industries and project types, from data-focused solutions to full digital products. Their feedback offers a broader view of what it is like to work with Beetroot.

  • Gautham Ramachandra,
    Restoration Ecologist, Land Life

    Beetroot has managed the process really well and delivered what they promised. There’s a lot of transparency in working with the team. They’re realistic about what we can expect, and they’ve tailored the product to our needs.

Featured Cases

Building time series forecasting software solutions calls for experience with data-heavy products, ML systems, and dependable engineering workflows. The cases below show those capabilities in action across healthcare, research, climate tech, and life sciences.

  • Predictive Analytics for Renewable Energy

    After 12 months in production, the forecasting module achieved 85–89% accuracy for 24-hour demand and solar generation predictions. It also reduced manual grid-balancing interventions by 10–12%, lowered reserve-energy purchasing costs by 8%, and increased local renewable energy use during peak hours by 9%.

    Read the full story

    • Python
    • FastAPI
    • AWS
    • Docker
    • PyTorch
    • Prophet
    • Apache Airflow
    • PostgreSQL

Custom AI & Data Workshops

Strengthen your team’s forecasting and data capabilities through hands-on, expert-led training. Our workshops are built around your current tools, datasets, and business priorities to help teams turn knowledge into practical results faster.

  • Sustainable Competitive Advantage

    • Learn from practicing engineers

      Each session is led by Beetroot’s senior AI and data specialists who bring real project experience across forecasting, machine learning, and analytics.

    • Solve real business challenges

      Workshops are designed around your live projects or current obstacles, such as improving model accuracy, scaling data pipelines, or integrating AI into production.

    • Build internal expertise and independence

      We focus on practical upskilling that lasts beyond the session. Teams gain the confidence and technical know-how to manage, evaluate, and evolve AI and forecasting solutions internally, reducing reliance on external vendors.

Have a forecasting project in mind? Let’s talk about it:

Whether you’re exploring predictive analytics, building custom forecasting models, or need expert guidance on data strategy, we’re here to help. Fill out the form to share your goals and challenges, and our team will get back to you to discuss how our time series forecasting services can support your business.

    FAQs

    Before starting a forecasting project, it helps to understand the practical requirements around data, accuracy, integration, and model upkeep.

    Nick Tykhomyrov, CBDO, Beetroot

    Nick Tykhomyrov

    CBDO