Time Series Analysis Experts in Munich
for accurate forecasts, matched in minutes with the power of AIHire experts who model demand, detect anomalies and build reliable forecasting pipelines with Python, R, pandas, statsmodels or Prophet. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Munich, who have recently used Time Series Analysis
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Thomas H.
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
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.
Martin R.
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Gurpreet D.
Last position:
Anonymous – German building materials manufacturer
- Creation of the profit center report and statistical metrics report
- Gathering and analysis of requirements regarding the forecast for revenue and costs
- Implementation of various time series analyses and optimization of forecasts
- SAP process knowledge, SQL, Python
- Python with various libraries for time series analysis and Machine Learning
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.
Utku U.
Last position:
Combining Neural Fields with Hypernetworks
- Developed a meta-learning approach with a teammate to merge multiple neural fields into a single scene representation using a hypernetwork.
- Implemented and evaluated the method on 2D (MNIST) and 3D (ShapeNet) data, showing faster inference compared to overfitting-based baselines.
Discover over 15,000 top freelancers
Statistics of experts using Time Series Analysis
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 13 years)
Position duration
2.3 years (Germany: 1.9 years)
Positions per freelancer
11 (Germany: 9)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100% (Germany: 94%)
Doctorate
20% (Germany: 35%)
Certifications per freelancer
4 (Germany: 2)
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 Munich 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 Munich using Time Series Analysis
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.
Time Series Analysis 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 (82%)
- Automotive (45%)
- Education (45%)
- Manufacturing (45%)
- Professional Services (45%)
- Aerospace and Defense (36%)
- Energy (36%)
- Banking and Finance (36%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What It Covers
Time series analysis examines observations recorded over time to reveal trend, seasonality, cycles and unusual events. It supports forecasting, monitoring and planning for values such as demand, revenue, sensor readings, energy use and application traffic. The work combines statistical reasoning with careful handling of timestamps, gaps and changing patterns.
Typical Applications
Companies use this discipline when past behavior can inform an operational or commercial decision. Common deliverables include:
- Demand and inventory forecasts for retail, logistics and manufacturing
- Anomaly detection for equipment, transactions and digital services
- Capacity planning for energy, transport and cloud infrastructure
- Forecasting pipelines for finance, operations and customer activity
Methods And Tools
The ecosystem ranges from classical statistics to machine learning. Specialists may use moving averages, exponential smoothing, ARIMA, state-space models and decomposition alongside gradient boosting or neural networks. Python commonly brings pandas, NumPy, statsmodels, scikit-learn and Prophet; R users often work with forecast, tsibble and fable. SQL, notebooks, Git and cloud data services support production workflows.
When Expertise Helps
Freelance expertise is valuable when forecasts must move from an experiment into a dependable business process. A specialist can select a suitable time frequency, define a clean target, prevent leakage, create backtesting procedures and explain uncertainty to stakeholders. In Munich, this can support local manufacturing, mobility, energy, retail and financial operations while fitting either on-site collaboration or a remote delivery model.
Project Signals
Bring in a specialist when historical data is available but decisions still rely on spreadsheets or intuition. Other signs include unstable forecasts, unexplained seasonal effects, recurring alerts without a clear cause or a model that works in a notebook but fails in production.
- Forecasts need a repeatable refresh and monitoring process
- Multiple data sources use inconsistent timestamps or calendars
- Business teams need prediction intervals, not just point estimates
- A pilot must be evaluated against a meaningful baseline
Strong Professionals
Strong professionals connect statistical choices to the decision the forecast will guide. They compare simple baselines with more complex methods, test performance on realistic rolling windows and document assumptions, data quality and failure cases. They also understand deployment, retraining, drift monitoring and communication, so a model remains useful after handover. Clear German or English communication can help teams in Munich coordinate across technical and business functions.
Frequently asked questions
Quick answers to the questions that come up most around Time Series Analysis.
Time Series Analysis is used to study measurements collected in time order and identify patterns that support forecasting or detection. Companies apply it to demand planning, energy loads, sensor monitoring, financial signals, staffing needs and service capacity.
Time Series Analysis accounts for ordering, autocorrelation, seasonality and changing behavior across time. Ordinary regression can still be useful, especially with external variables, but it needs careful validation when observations are not independent.
A strong Time Series Analysis specialist usually understands data modeling, SQL, Python or R, statistical testing and visualization. Experience with cloud storage, workflow orchestration, APIs, version control and model monitoring is valuable when forecasts must run in production.
The right level for Time Series Analysis depends on data quality, forecast horizon, business risk and delivery scope. A focused exploratory project may need a specialist who can establish a sound baseline, while a production system benefits from experience with backtesting, deployment and ongoing monitoring.
Yes, Time Series Analysis work is often suitable for remote collaboration when data access, documentation and review routines are well defined. On-site workshops can help with domain discovery, while secure remote access and clear communication usually support the modeling and implementation work.
A credible Time Series Analysis project uses a time-aware holdout or rolling backtest rather than a random split. Review the chosen baseline, error measure, prediction intervals, behavior across important segments and the plan for monitoring drift after release.
Time Series Analysis often starts with seasonal naïve forecasts, moving averages or exponential smoothing because they are transparent and hard to beat on limited data. More complex models make sense when they produce consistent improvement, use meaningful external signals or handle patterns that simpler methods miss.
Reliable Time Series Analysis requires consistent timestamps, suitable aggregation, documented missing values and protection against information from the future. Specialists should also check changing product definitions, calendar effects, outliers and sudden operational changes before selecting a model.
The average hourly rate of freelancers in Munich, Germany who have used Time Series Analysis in their recent projects is 92 €, which corresponds to a daily rate of about 733 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Time Series Analysis in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Munich, Germany who have used Time Series Analysis in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Munich, Germany who have used Time Series Analysis in their recent projects are German (100%), English (100%), and French (36%).
The most common industries among freelancers in Munich, Germany who have used Time Series Analysis in their recent projects are Information Technology (82%), Automotive (45%), and Education (45%).
The most common business areas among freelancers in Munich, Germany who have used Time Series Analysis in their recent projects are Information Technology (91%), Business Intelligence (82%), and Product Development (73%).
Main locations of FRATCH Experts, who have recently used Time Series Analysis
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 
Berlin 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
