ARIMA Experts in Germany
in minutes from 15,000 CVs with the power of AIHire experts who build ARIMA, SARIMA, and AutoARIMA forecasting for demand, finance, and operations data. They clean time series, tune models, and validate forecasts with clear error checks. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used ARIMA
Raphael Mankopf
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 Ali
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 Castillo
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 Karimzadeh Sharifabadi
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.
Chaima Dahri
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 Fatemi
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 Tosun
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 Negi
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 Verma
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 Daube
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 Bhatia
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 Chtourou
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 Mauch
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
9 years
Position duration
1.9 years
Positions per freelancer
6
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, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
67%
Doctorate
8%
Certifications per freelancer
3
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
ARIMA basics
ARIMA is a time series method for forecasting data that changes over time. It combines past values, past errors, and differencing to handle trend and short-term autocorrelation. Companies use it for demand planning, revenue tracking, inventory signals, and other series with a stable historical pattern.
Where it fits
- Forecasting sales, orders, and usage
- Sizing stock and replenishment plans
- Tracking finance, traffic, or sensor trends
- Supporting what-if analysis with clear assumptions
ARIMA works best when the series is well understood and the goal is a practical forecast, not a black-box system.
Related methods
Strong specialists usually know SARIMA for seasonality, AutoARIMA for model search, and basic statistical testing. They also understand stationarity, residual checks, and lag selection. In Python, they often work with statsmodels, pmdarima, pandas, and notebook-based analysis.
What good work looks like
A solid deliverable is not just a fitted model. It includes clean data preparation, a reasoned choice of parameters, backtesting, and a clear explanation of forecast limits. Good experts show when ARIMA is enough and when a simpler or more advanced approach is safer.
When companies bring in help
Companies usually look for freelance ARIMA expertise when a forecast must be delivered quickly, an old model needs review, or internal teams need a better baseline. It is also common when the data is noisy, seasonal, or split across regions like Germany with different demand patterns.
Why strong specialists stand out
- They handle missing values and outliers carefully
- They test stationarity before modeling
- They compare against naive and seasonal baselines
- They explain results in plain business terms
The best professionals know that a forecast is only useful if it can be trusted, reviewed, and repeated.
Frequently asked questions
Not sure where to start with ARIMA? These answers cover the essentials.
ARIMA is used to forecast time series that have trend, autocorrelation, or both. Companies use it for demand planning, inventory signals, revenue tracking, call volumes, traffic patterns, and similar data that arrives in order over time. It is a strong choice when the historical pattern is clear and the forecast needs to be explainable.
ARIMA is the core model family. SARIMA adds seasonal terms for repeating patterns such as weekly or yearly cycles, while AutoARIMA helps search for candidate settings automatically. A good specialist knows when the plain model is enough and when seasonality makes SARIMA the better fit.
A strong ARIMA specialist should also handle data cleaning, stationarity checks, lag analysis, and forecast validation. Python with pandas and statsmodels is common, and many professionals also use pmdarima for automated model selection. Business sense matters too, because the model must match the planning question.
An ARIMA project can be simple or demanding depending on the data quality and the business goal. A small baseline forecast may need only focused statistical work, while messy or seasonal data needs deeper modeling judgment. The real question is whether the expert can defend the model choice and the forecast error.
Choose ARIMA when the series is the main signal, the history is reasonably stable, and interpretability matters. Machine learning can help when there are many external drivers, but it is not always the best first step. For many business forecasts, a well-tuned ARIMA or SARIMA baseline is still the most practical start.
Most ARIMA work can be done remotely because the main tasks are data review, modeling, testing, and documentation. On-site time only helps when the forecast depends on close stakeholder input or sensitive internal data access. For teams in Germany, remote collaboration usually works well if the expert can communicate clearly in the agreed language.
Look for a ARIMA specialist who explains differencing, seasonality, residual behavior, and forecast limits without hiding behind jargon. Good work includes backtesting, benchmark comparisons, and a clear reason for choosing the final model. If the answer is only "the model fit well," that is not enough.
Yes, ARIMA is still relevant because many business problems need a dependable, transparent baseline. It is often the first model used to set a standard before more complex methods are tested. In many cases, the value is not novelty but a forecast that can be audited and trusted.
The average hourly rate of freelancers in Germany who have used ARIMA in their recent projects is 94 €, which corresponds to a daily rate of about 750 € 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, 67% hold at least a Master's degree, and 8% hold a doctorate.
On average, freelancers in Germany who have used ARIMA in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Germany who have used ARIMA in their recent projects are German (100%), English (100%), and Arabic (15%).
The most common industries among freelancers in Germany who have used ARIMA in their recent projects are Information Technology (85%), Automotive (54%), and Banking and Finance (46%).
The most common business areas among freelancers in Germany who have used ARIMA in their recent projects are Information Technology (92%), Business Intelligence (85%), and Product Development (85%).
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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Philipp Thomaschewski
FRATCH CEO
