Case Study

Time Series Forecasting For Everyone!

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%

Improvement in Forecasting Accuracy

%

Reduction in Model Deployment Time

%

Fewer Forecasting Errors



Predictive Intelligence

Hands-free model selection that delivers high-accuracy forecasts and flags unusual patterns instantly

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Forecasts in Minutes

Accelerate decision-making with instantly deployable forecasting solutions.

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70% increase in operational efficiency

Centralized data handling, seamless integration, and minimal manual effort lead to streamlined workflows and better resource use.

Client Overview

Odyssey Solutions is an offshore development company set on a mission to make tomorrow’s innovative technologies accessible in the present day.

They wanted Techzooni to create Odyx yHat to offer accuracy, ease & usability for conducting Time Series Forecasting on the go. Moreover, the user can predict spot & forward prices efficiently, assess users’ consumption habits & predict accurate demand prices.

Client: Odyssey Solutions

Industry: IT company

Project Duration: 5 Months

Services Provided: Artificial Intelligence, Machine Learning, UI/UX

Challenges

5

Automated Feature Engineering for Time Series

Identifying and creating relevant time-based features (lags, rolling stats, seasonality indicators, etc.) without human intervention is complex and domain-dependent.

5

Handling Diverse Data Formats & Quality

Users may upload data in varying formats and structures. Designing a system that can robustly clean, validate, and standardize time series data is a major challenge.

5

Model Selection & Hyperparameter Optimization

Automatically selecting the best forecasting model (ARIMA, Prophet, XGBoost, LSTM, etc.) and tuning its parameters for different industries and data types requires advanced AutoML logic.

5

Scalability Across Multiple Forecasting Projects

Supporting unlimited, parallel forecasting projects without degrading model performance or speed involves significant challenges in both compute efficiency and model architecture.

5

Balancing Accuracy vs. Usability

Highly accurate models often require customization and tuning. Striking the right balance between out-of-the-box accuracy and maintaining a simple, no-code user interface is a major product design and data science challenge.

5

Forecasting at Different Granularities

Users may want forecasts by day, week, month, etc. Supporting flexible aggregation levels while maintaining forecast quality is both technically and mathematically complex.

Solution

5

Smart Feature Generation

Integrate tools like SHAP, feature contribution charts, and model diagnostics dashboards to help users understand the “why” behind forecasts — all without requiring technical expertise.

5

Intelligent Data Cleaning

Apply transfer learning or use pre-trained base models fine-tuned to industry-specific patterns. For small datasets, fall back on simpler, interpretable models like exponential smoothing or seasonal naïve.

5

Project Scalability Engine

Leverage containerized deployment (e.g., Docker + Kubernetes) and scalable cloud infrastructure to handle multiple concurrent projects while maintaining speed and performance.

5

Transparent Predictions

Integrate tools like SHAP, feature contribution charts, and model diagnostics dashboards to help users understand the “why” behind forecasts — all without requiring technical expertise.

5

Auto Model Tuning

Use a model zoo (ARIMA, Prophet, XGBoost, LSTM, etc.) with automated model benchmarking. Behind the scenes, deploy Bayesian or evolutionary optimization to tune hyperparameters for each dataset.

5

Drift Detection & Retraining

Implement drift detection algorithms that monitor changes in data distribution. Set up automated retraining triggers and model versioning to keep forecasts up-to-date and relevant.

Features

Key Features Developed

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Multiple Data Source Connection

Connect with any type of data source( third-party database, API interface, or simply upload raw data).

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Several Model Variations

The tool automatically selects suitable models from various forecasting models.

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No-Code Approach

Rolling out forecasting models for real-world applications.

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Explorartory Data Analysis Engine

Dig into your data and find clear, powerful insights with ease.

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Versatile Data Processing Methods

Easily classify data, predict trends, and solve complex regression and time series tasks.

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Real-Time Forecast Monitoring

Continuously track forecast performance and receive instant alerts when deviations or anomalies are detected.

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Technologies

Frontend

React Native, Google Maps SDK

Backend

Node.js, Express.js

Database

MongoDB

Security

OAuth 2.0, End-to-End Encryption

Authentication

FireBase Authentication

Cloud & Infrastructure

AWS (EC2, S3), Cloudflare

Conclusion

Odyx yHat stands as a modern solution to the evolving challenges of time series forecasting. By combining intelligent automation with a no-code interface, multi-source data connectivity, and powerful analytical tools, it empowers users to make faster, smarter, and more confident decisions—regardless of their technical background. Designed for professionals across industries, Odyx yHat is not just a forecasting tool—it’s a complete decision support system that evolves with your data needs.

As someone who isn’t a data scientist, Odyx yHat has been a total game-changer for me. I can connect my data, run forecasts, and actually understand what the results mean—all without writing a single line of code. It’s fast, intuitive, and incredibly reliable. I genuinely don’t know how we managed forecasting before this!

Ayesha Ali

Portfolio Strategy Lead, Odessy Solutions