ARIMA Experts in Germany
for reliable forecasts, matched in minutes with vetted freelance specialistsHire experts who design, validate and operationalize ARIMA forecasting models for demand planning, sales projections, energy loads and time-series analysis. Get precisely matched with vetted, available freelancers who can support your project quickly.
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.
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
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.
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.
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
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.
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.
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.
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)
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 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
Forecasting with ARIMA
ARIMA, short for AutoRegressive Integrated Moving Average, is a statistical method for forecasting time-series data. It models relationships between past observations, differences and past forecast errors. Specialists use it when historical patterns contain useful signals and the data can be prepared into a suitable stationary form.
Typical applications
ARIMA supports forecasts where timing and sequence matter. It is often applied to operational, financial and commercial planning:
- Demand and inventory forecasting
- Sales, revenue and cash-flow projections
- Energy consumption and load planning
- Capacity, traffic and service-volume forecasts
- Monitoring and anomaly investigation
Models and tooling
ARIMA work commonly involves Python libraries such as statsmodels, pmdarima and pandas, or R packages including forecast and fable. Strong specialists understand autoregressive and moving-average terms, differencing, seasonal extensions such as SARIMA, and external regressors in ARIMAX models. They also build repeatable data preparation and evaluation workflows.
When companies need expertise
Companies bring in freelance ARIMA specialists when forecasts are inconsistent, internal teams need a defensible modelling approach, or a prototype must become a reliable planning service. In Germany, this can support manufacturing, logistics, retail, finance and energy teams. Remote collaboration works well when data access, business context and review routines are clearly organized; on-site work can help with stakeholder workshops and process discovery.
What good delivery includes
A capable professional checks data quality before selecting a model. They investigate missing periods, outliers, changing variance, calendar effects and structural breaks instead of treating every pattern as stable. Their deliverables may include a documented pipeline, reproducible training process, forecast intervals, backtesting results and guidance for monitoring model drift.
Choosing the right specialist
Look for evidence of sound statistical reasoning and practical delivery, not only familiarity with the ARIMA acronym. A strong specialist can explain why differencing is needed, compare ARIMA with exponential smoothing or machine-learning models, and connect forecast accuracy to a business decision. They should also communicate assumptions clearly and make handover simple for data, product and operations teams.
Frequently asked questions
Not sure where to start with ARIMA? These answers cover the essentials.
ARIMA is used to forecast values recorded over time, such as demand, sales, energy use, cash flow or service volume. It can help teams plan resources and identify unusual changes when past observations contain relevant patterns.
ARIMA models autocorrelation through past values, differencing and forecast errors, while exponential smoothing focuses on weighted level, trend and seasonal components. The better choice depends on the data, forecast horizon, seasonality and the business need for interpretability.
ARIMA can be a strong choice when the main signal is in a single time series and the data history is reasonably structured. Machine-learning models may be more suitable when many explanatory variables, complex interactions or large collections of related series drive the forecast.
A strong ARIMA specialist should also understand time-series diagnostics, data cleaning, SQL or Python, statistical validation and visual communication. Experience with SARIMA, ARIMAX, forecast intervals and deployment workflows is useful when a model must support ongoing operations.
The right level depends on the task. A short modelling assignment may need someone who can prepare data and validate forecasts, while a production system requires deeper experience with data pipelines, monitoring, retraining and communication with business stakeholders.
ARIMA projects are often well suited to remote collaboration because data preparation, modelling and review can be handled through shared repositories and virtual meetings. German-language communication may matter for workshops, documentation or stakeholder coordination, while on-site sessions can help clarify operational processes.
Ask the ARIMA specialist to show how they assessed stationarity, selected model terms and tested performance on periods held out from training. Quality also depends on sensible forecast intervals, transparent assumptions and comparison with a simple baseline or relevant alternative.
Before using ARIMA, the freelancer should clarify the forecast target, time frequency, horizon, available history, data revisions and the decision the forecast will support. They should also confirm how accuracy will be measured, how often forecasts are refreshed and who will act on the output.
The average hourly rate of freelancers in Germany who have used ARIMA in their recent projects is 88 €, which corresponds to a daily rate of about 706 € 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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
