You will get Anomaly Detection of Time Series
Project details
You will receive a meticulously developed, research-backed anomaly detection model for time-series data analysis. Drawing upon over 3 years of focused experience in AI data science research, I deliver a solution grounded in robust methodologies designed to meet academic inquiry's rigorous demands.
Machine Learning Tools
Microsoft Excel, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, TensorFlowWhat's included $400
These options are included with the project scope.
$400
- Delivery Time 30 days
- Number of Revisions 2
- Number of Model Variations 1
- Number of Scenarios 1
- Number of Graphs/Charts 5
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Additional Revision
+$10
Additional Scenario
(+ 3 Days)
+$10
Additional Graph/Chart
(+ 3 Days)
+$3About Haidar
About me
Yogyakarta, Indonesia - 11:50 am local time
I lived in Australia for 15 years and after I finish my bachelor I
continued my studied and lived in Taiwan for 2 years.
My experience outside of Indonesia not only taught me about the
different aspect in cultures but also in attitude. For me attitude is
important in a working environment showing honesty and respect to
one another and build relationships with co-worker is my main strife
in achieving a better work environment for my organization/company
Most of my work experience are all related in academic as I mostly in
computer labs doing research.
Steps for completing your project
After purchasing the project, send requirements so Haidar can start the project.
Delivery time starts when Haidar receives requirements from you.
Haidar works on your project following the steps below.
Revisions may occur after the delivery date.
Problem Scoping: Defining the Anomaly Detection Objective
Define time-series data type and anomaly characteristics. Establish clear objectives and success criteria for anomaly detection, aligning with business goals (e.g., fault identification, fraud detection).
Data Acquisition & Preprocessing: Sourcing and Preparing Signal Data
Collect time-series signal data, identifying variables and class structures. Perform comprehensive data cleaning (missing values, outliers, noise) and prepare data (normalization/standardization) for neural network training.