Time Series Analysis Experts in Munich
for reliable forecasts, anomaly detection and fast AI matchingHire experts who turn chronological data into forecasts, alerts and decision-ready models, using statistical methods, Python, R and tools such as Prophet or statsmodels. FRATCH matches you quickly with vetted, available freelancers who fit your project.
Meet FRATCH Experts in Munich, who have recently used Time Series Analysis
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
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)
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
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
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
2.2 years (Germany: 1.9 years)
Positions per freelancer
11 (Germany: 8)
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
18% (Germany: 34%)
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 Munich 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 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 29 Oct 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 (83%)
- Automotive (50%)
- Education (50%)
- Energy (42%)
- Manufacturing (42%)
- Professional Services (42%)
- Aerospace and Defense (33%)
- Banking and Finance (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
Time Series Analysis examines observations recorded over time, such as demand, energy use, prices, traffic or machine signals. Specialists identify trend, seasonality, cycles, autocorrelation and changing variance to explain what happened and estimate what comes next. The work can support forecasts, monitoring, planning and automated decisions.
Typical use cases
- Forecast sales, inventory demand and staffing needs
- Detect unusual transactions, sensor readings or service behaviour
- Model energy consumption, load profiles and renewable generation
- Predict maintenance needs from equipment telemetry
- Analyse financial, operational and customer activity over time
The right method depends on the data frequency, forecast horizon, business cost of errors and stability of the underlying process. A useful solution connects model output to a decision, alert or workflow rather than stopping at a chart.
Methods and tools
Professionals work with classical approaches such as ARIMA, SARIMA, exponential smoothing and state-space models, as well as regression with lagged variables and machine learning. Python ecosystems often include pandas, NumPy, statsmodels, scikit-learn and Prophet; R users may work with forecast, fable and tidyverts. Deep learning can help with complex, high-volume signals when the data and operating conditions justify it.
Data foundations
Good results start with reliable timestamps, consistent time zones, suitable frequency and clear handling of missing values, duplicates and outliers. Experts separate training and validation by time, prevent leakage from future information and test forecasts against practical baselines. They also examine structural breaks, promotions, weather, holidays and other external drivers that affect the series.
When to bring in specialists
- Forecasts remain unstable despite repeated model changes
- Teams need anomaly detection for live operational data
- A proof of concept must become a monitored production service
- Many related series require a consistent modelling approach
- Business teams need forecast uncertainty they can act on
Freelance expertise is useful when a company has valuable time-based data but lacks the capacity to design experiments, compare methods and deploy results. In Munich, collaboration may combine remote delivery with on-site workshops for manufacturing, mobility, energy, finance or retail teams.
What strong experts deliver
Strong professionals define the target and forecast horizon with stakeholders before choosing a model. They provide reproducible data preparation, transparent evaluation, prediction intervals, documentation and monitoring for drift or degraded accuracy. They can explain why a model is trusted, when it should be retrained and how users should respond to uncertainty.
They also understand the systems around the analysis: SQL warehouses, APIs, notebooks, cloud storage, orchestration and dashboards. For remote or on-site work in Munich, clear communication in English or German can help connect statistical findings with operational decisions.
Frequently asked questions
Quick answers to the questions that come up most around Time Series Analysis.
Time Series Analysis is used to understand and forecast data ordered by time. Common applications include demand planning, financial signals, energy load, equipment monitoring, traffic flows and anomaly detection.
Time Series Analysis accounts for time order, dependence between observations, seasonality and changing conditions. Ordinary machine learning can still be useful, especially with external variables, but random train-test splits often produce misleading results for time-based data.
A strong Time Series Analysis specialist usually combines statistics with Python or R, SQL, data preparation and visualisation. Experience with APIs, cloud data systems, forecasting services and model monitoring is valuable when analysis must run in production.
The required experience depends on the data quality, forecast horizon, number of related series and consequences of errors. A small exploratory analysis may need focused modelling expertise, while a production forecasting system also calls for validation design, deployment and monitoring skills.
Time Series Analysis is well suited to remote collaboration when data access, documentation and review processes are organised. Munich-based teams can use remote delivery for modelling and workshops, with on-site sessions where domain knowledge, factory signals or operational workflows are easier to examine in person.
Time Series Analysis often benefits from ARIMA, exponential smoothing or state-space methods when the dataset is limited or the patterns are interpretable. Deep learning may be appropriate for many related series and complex signals, but it needs careful validation and does not automatically improve a forecast.
Ask for a time-based validation plan, a meaningful baseline, error measures suited to the business and an explanation of uncertainty. Quality work also documents missing-data treatment, leakage controls, external drivers, retraining triggers and how forecast performance will be monitored.
A Time Series Analysis professional should clarify the decision the forecast supports, the forecast horizon, data frequency, available history and acceptable error. They should also confirm access constraints, time zones, business events, deployment expectations and who will act on alerts or predictions.
The average hourly rate of freelancers in Munich, Germany who have used Time Series Analysis in their recent projects is 88 €, which corresponds to a daily rate of about 708 € 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 18% 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 2.2 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 (42%).
The most common industries among freelancers in Munich, Germany who have used Time Series Analysis in their recent projects are Information Technology (83%), Automotive (50%), and Education (50%).
The most common business areas among freelancers in Munich, Germany who have used Time Series Analysis in their recent projects are Information Technology (92%), Business Intelligence (83%), and Product Development (75%).
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
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Philipp Thomaschewski
FRATCH CEO
