ARIMA Experts in Germany
for reliable forecasts, matched in minutes with vetted and available freelancersHire experts who build demand forecasts, time-series models and monitoring workflows with ARIMA, Python or R, and statistical validation. Get precisely matched with vetted, available freelancers who can support remote or on-site work in Germany.
Meet FRATCH Experts in Germany, who have recently used ARIMA
Hakan A.
Last position:
Senior Software Engineer — AI Evaluation & Benchmarks at Diversido
- Provided technical leadership for a 4-engineer team delivering 3 major client platforms in 12 months with microservices architecture and scalability solutions — 100% of scoped majors shipped ahead of schedule vs. planned milestones (baseline: prior releases often slipped 1–2 sprints).
- Ran AI model evaluation and model outputs evaluation on LLM/AI vendor APIs: safety, completeness, instruction adherence, and groundedness review before go-live; cut escaped bad outputs in AI-integrated release checklists from recurring UAT findings to near-zero on final promote.
- Drove API development and performance optimization for payment, exchange, and AI services; fail-closed error handling and payload validation reduced integration rework cycles by ~35% vs. the first AI integration pass.
- Applied software testing, testing frameworks, code quality assurance, and code refactoring with continuous integration gates; first-pass PR acceptance improved across the team and production hotfixes on AI adapters dropped noticeably after review standards landed.
- Owned DevOps practices: Docker, GitHub Actions, Jenkins-compatible pipelines, and version control workflows — cut deployment time ~50% vs. pre-automation baseline and stabilized releases across 3 client environments.
- Implemented verifier/oracle-style pass-fail checks in container sandboxes (Harbor/Terminal-Bench aligned); wrote technical documentation so failures cleared in one review cycle.
- Led cross-functional collaboration with product and client stakeholders; translated AI evaluation scores and risk findings into plain-language briefs for non-technical partners, unblocking go/no-go decisions without extra engineering meetings.
- Used agile methodologies for sprint planning and backlog ownership; mentored engineers so mid-level contributors owned AI adapter modules independently by mid-engagement.
Xinyang M.
Last position:
Sales Operations Analyst Intern at Capgemini
- Developed and maintained 5 Power BI dashboards for pipeline tracking, forecasting, revenue-gap, quota-achievement, and deal-performance analysis.
- Delivered weekly, monthly, and quarterly reporting used by approximately 100 stakeholders across Sales, Finance, and Marketing.
- Built semantic data models and ETL workflows using Power Query, DAX, and SQL; integrated Salesforce, SharePoint, internal data warehouse, and Excel sources.
- Automated data ingestion, cleaning, transformation, format standardization, KPI calculations, dashboard refresh, and reporting preparation using Power Query, DAX, and Power BI, eliminating several hours of recurring manual data preparation and reporting work.
- Standardized KPI calculations and built interactive reports with row-level security, drill-through, and Waterfall analysis for business reviews and forecasting.
Raphael M.
Last position:
Founder / Quant Developer at Market Maker
- Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
- Data and trade architecture development for liquidity provision
Sara A.
Last position:
Research Associate and Data Scientist at National Center of Robotics and Automation - Condition Monitoring Lab
- Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
- Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
- Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
- Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.
Geraldine C.
Last position:
Solution Engineer (Data & ML Integration) at Amadeus Data Processing GmbH
- Designed ML-ready data integration workflows between on-premise systems and cloud platforms (Snowflake, AWS Redshift, Azure), enabling scalable feature engineering and model deployment
- Implemented automated ML pipeline deployment using Python, SQL, and CI/CD tools, reducing model deployment time by 60%
- Developed data transformation logic for master data synchronization across ERP and analytics systems, ensuring data quality for predictive models
- Collaborated with cross-functional teams to translate business requirements into mathematical specifications for ML solutions
Atefeh K.
Last position:
Freelance AI Trainer at Outlier
- Designing and optimizing prompts for AI and machine learning models to improve reasoning, problem-solving, and scientific accuracy.
- Evaluating model performance and providing structured feedback to enhance consistency, reliability, and interpretability.
- Applying data-driven insights to refine AI outputs for technical and scientific applications.
- Developing practical experience in Machine Learning, AI evaluation, and prompt engineering for scientific use cases.
Martin M.
Last position:
Freelance Data Architect at Zeppelin
- Evaluation and scoring of various technologies as future telematics platform (Kafka Streams, Spark, Splunk, Snowflake)
- Improve test framework and scalability of Telematics streaming service (Scala, Property-Based Testing, Kafka, Kafka Streams, Kubernetes)
Chaima D.
Last position:
Data Scientist Intern at Marelli Automotive Lighting
- Developed and deployed a deep learning model for automated keypoint detection in headlamp light distributions.
- Prepared and processed datasets, and selected VGG16 after benchmarking CNN architectures for the best accuracy efficiency trade-off.
- Delivered a Flask REST API, containerized with Docker, and integrated the solution into an existing internal system, enabling automated and efficient evaluation of headlamp designs.
Niowsha F.
Last position:
Machine Learning Research Assistant (HiWi) at DIGIT
- Train and optimize MAVAE/VAE models in PyTorch to detect anomalies in multivariate time-series sensor data.
- Design preprocessing workflows and evaluation pipelines to improve model accuracy and robustness.
- Benchmark MAVAE performance against baseline statistical and deep learning approaches, presenting comparative insights.
- Collaborate with research supervisors to refine hypotheses and translate experimental findings into deployable research outputs.
İlayda T.
Last position:
Data Analysis Expert at Turkish Statistical Institute
- I began my career at the National Statistics Office as an Assistant Expert and was later promoted to Expert
- Specialized in analyzing official statistics and handling complex datasets to extract meaningful insights
- Successfully managed and coordinated over 20 ongoing projects annually, collaborating with cross-functional teams to drive data-driven decision-making and process optimization
- Conducted seasonal adjustment analysis using JDemetra+ for over 1,000 time series annually, including GDP, foreign trade and consumer confidence indices
- Applied forecasting, backcasting and nowcasting techniques for time series, analyzing complex datasets and high-frequency time series
- Conducted econometric modeling to assess economic trends and policy impacts, applying statistical techniques to improve forecasting accuracy
- Built statistical models, including ARIMA models, determining key variables using both statistical tests and economic significance
- Ensured data integrity by detecting anomalies, cleaning datasets, performing outlier detection and improving data quality across databases
- Automated data preprocessing and transformation workflows using Python and SQL, reducing manual effort and improving efficiency
- Developed dashboards and reports in Excel and R Markdown to visualize and present results effectively
- Assisted other departments with data analysis needs and provided training on data analysis, time series and seasonal adjustment
- Prepared methodology reports for official statistics and communicated findings and insights to both technical and non-technical stakeholders
- Collaborated with international partners (EUROSTAT, ICON Institute) to harmonize methodologies
- Worked on statistics including foreign trade indices, gross domestic product, labour force statistics, foreign trade statistics, turnover indices, industrial production index, consumer price index, consumer confidence, labour input, labour cost and earnings statistics, retail sales indices, services, retail trade and construction confidence
Himanshu N.
Last position:
Principal (Data Scientist/Data Engineer/Gen AI Engineer) at Marktguru Deutschland GmbH
Architected an agentic, real-time offer orchestration engine where specialized agents (retrieval, pricing/optimization, and policy/guardrails) coordinate to personalise promotions across customer touchpoints using RAG with FAISS over Delta Lake and low-latency Databricks Model Serving. Collaborated with product managers and commercial stakeholders to shape the roadmap and evaluate emerging agent patterns for production.
Designed an agent-based data quality service that orchestrates schema detection, entity normalization, and validator/exception-handling agents to clean multi-retailer SKU feeds at scale. Wrapped model calls in PySpark UDFs for distributed inference, automated via Databricks Workflows and CI/CD.
Developed a multimodal, agentic extraction pipeline where vision, parsing, and compliance agents collaborate to derive brand, packaging, and volume from scanned images using Claude 3 Sonnet with Swin Transformer encoders. Orchestrated via Azure Event Hub with outputs persisted to Delta Lake.
Implemented a GS1 taxonomy classification service built around cooperating agents for inference, drift monitoring, and auto-retraining governance using Falcon 180B (LoRA-tuned) with a batch pipeline on Databricks.
Created a hybrid agent workflow where a retrieval agent surfaces candidate matches via embeddings and a reasoning/verification agent (Mixtral 8x7B) adjudicates receipt-to-SKU alignment, integrated into a streaming Databricks pipeline.
Built a multimodal attribute inference pipeline structured as cooperating vision-language, rules/consistency, and compliance agents to fill NutriScore, nutrition fields, and packaging types from names and images using LLaMA 3-8B with CLIP embeddings.
Developed a GenAI-powered orchestration system that ingests recipes from multiple websites, parses ingredients through structured extraction agents, and dynamically links them to real-time retailer offers via tagging, semantic reasoning, and business-rule agents.
Muskan V.
Last position:
AI Engineer at Sagas IT Analytics
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
- Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
- Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
- Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
- Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
- Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
- Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Vasuraj B.
Last position:
Cloud Data Analyst at Bhatia Reply
- Analyzed 50K+ customer records using SQL and Python in a cloud services firm, identifying trends
- Designed interactive Tableau dashboards for sales and marketing stakeholders, reducing report
- Developed ARIMA and AutoARIMA time series models to forecast AWS resource utilization, cutting
- Automated ETL pipelines with Python, improving workflow efficiency by 20% for scalable data
- Collaborated with DevOps teams to deploy 3 machine learning models in production using Docker
Jasser C.
Last position:
Artificial Intelligence Intern at Plug&Plai
- Supported the development of AI-powered voice assistants for recruitment automation.
- Helped optimize speech recognition models and conversational AI workflows.
Discover over 15,000 top freelancers
Statistics of experts using ARIMA
Aggregated from the professional profiles of matched freelancers.
Experience
8 years
Position duration
1.8 years
Positions per freelancer
5
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Research and Development, Information Technology, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
71%
Doctorate
7%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using ARIMA
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
ARIMA experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (87%)
- Automotive (47%)
- Banking and Finance (47%)
- Professional Services (47%)
- Healthcare (40%)
- Manufacturing (27%)
- Education (20%)
- Transportation (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What ARIMA does
ARIMA stands for AutoRegressive Integrated Moving Average. It forecasts ordered observations by combining relationships with past values, differencing to address trends, and moving-average terms that represent past forecast errors. It is designed for time series where order, timing and historical behaviour matter.
Common applications
ARIMA is useful when a company needs an explainable forecast from a single measured series and has enough consistent history to model its behaviour.
- Demand and sales forecasting
- Inventory and replenishment planning
- Energy load and consumption prediction
- Capacity, traffic and operational planning
- Baseline forecasts for anomaly detection
Ecosystem and tooling
Professionals often implement ARIMA with Python libraries such as statsmodels, pmdarima and pandas, or with R packages including forecast and fable. Strong work also covers data preparation, stationarity checks, residual analysis, rolling validation and clear handover into notebooks, services or reporting workflows. In Germany, experts may also support teams that need remote delivery alongside occasional on-site collaboration.
When companies need help
Freelance expertise is valuable when an internal team has data but lacks time to establish a defensible forecasting process. It can also help when forecasts drift, model assumptions are unclear, or a prototype must become a repeatable service.
- Irregular timestamps or missing observations need careful treatment
- Existing forecasts need unbiased backtesting
- A model must connect to production data or business reporting
- Forecast intervals and failure conditions must be documented
What strong experts deliver
A capable ARIMA professional starts with the business decision, not just the model. They inspect seasonality, transformations, outliers and structural breaks, compare sensible baselines, and explain whether the data supports an ARIMA approach. They also make validation reflect the real forecasting horizon and provide reproducible code, assumptions and monitoring guidance.
ARIMA and alternatives
ARIMA is often weighed against exponential smoothing, seasonal ARIMA, Prophet, state-space models and machine-learning approaches. It can be a strong choice when the series has stable temporal structure and interpretability matters, but it is not automatically suitable for multiple external drivers, abrupt regime changes or many related series. A good professional selects the method through data testing rather than preference and can combine ARIMA with regression variables when the use case requires it.
Frequently asked questions
Not sure where to start with ARIMA? These answers cover the essentials.
ARIMA is used to forecast future values from the historical behaviour of an ordered time series. Companies apply it to demand, sales, energy use, inventory, traffic and other operational signals where timing and past forecast errors provide useful information.
ARIMA models relationships within a time series and can offer a clear statistical explanation. Exponential smoothing may be simpler for level, trend and seasonality, while machine learning can be stronger when many external variables or complex interactions drive the outcome.
A strong ARIMA freelancer should understand data cleaning, time-series validation, seasonality, stationarity, residual diagnostics and forecast intervals. Python or R, SQL, version control and the ability to expose results through a service or reporting workflow are also useful.
The required experience depends on data quality, forecast horizon and production demands rather than a fixed project duration. For a simple analysis, a specialist should still demonstrate sound validation; a production workflow needs experience with pipelines, monitoring, retraining and stakeholder communication.
ARIMA projects are often suitable for remote collaboration because data review, modelling and documentation can be shared securely online. On-site workshops in Germany may help when experts must align with planning teams, operational processes or German-language stakeholders.
ARIMA may be a poor fit when the series has little history, frequent structural changes or strong dependence on external drivers that are not included in the model. It may also be unsuitable when many related series must be forecast together without a broader multivariate approach.
A reliable ARIMA professional uses time-aware backtesting, compares simple baselines and checks residuals rather than presenting one attractive forecast. They should explain uncertainty, document transformations and show how performance will be monitored after deployment.
Box–Jenkins refers to the established process of identifying, estimating and diagnosing ARIMA models. It is not a separate forecasting technology; it describes a disciplined modelling approach that helps specialists choose a suitable structure and test whether its residuals are credible.
The average hourly rate of freelancers in Germany who have used ARIMA in their recent projects is 89 €, which corresponds to a daily rate of about 711 € based on an 8-hour working day.
Of the freelancers in Germany who have used ARIMA in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 7% hold a doctorate.
On average, freelancers in Germany who have used ARIMA in their recent projects have 8 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Germany who have used ARIMA in their recent projects are German (100%), English (100%), and French (20%).
The most common industries among freelancers in Germany who have used ARIMA in their recent projects are Information Technology (87%), Automotive (47%), and Banking and Finance (47%).
The most common business areas among freelancers in Germany who have used ARIMA in their recent projects are Information Technology (93%), Business Intelligence (87%), and Product Development (80%).
Main locations of FRATCH Experts, who have recently used ARIMA
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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FRATCH CEO
