Time Series Forecasting Model Development -- 2
Budget: $10 – $30 USD
Project Overview:
We are seeking an experienced data scientist or statistician to develop a forecasting model that predicts future trends based on our historical data. The goal is to build a robust and accurate model that can forecast incoming calls and workload over the coming 6 months. including identifying patterns, trends, and seasonality in our data.
Project Details:
Data Overview:
We have historical data spanning 2 years
Objectives:
Analyze and preprocess the historical data to identify trends, seasonality, and any anomalies.
Develop and test multiple forecasting models (e.g., ARIMA, exponential smoothing, Prophet, or machine learning approaches) to select the most accurate method.
Validate the model using appropriate metrics such as RMSE or MAPE.
Provide visualizations and a clear explanation of the forecasting results along with confidence intervals.
Scope of Work:
Data Preparation: Clean and preprocess the data to ensure quality input for the forecasting models.
Exploratory Analysis: Conduct a thorough analysis to understand trends, seasonality, and outliers.
Model Selection and Development: Experiment with different forecasting methods, tuning parameters to achieve optimal performance.
Validation: Compare model predictions against historical data and validate performance using standard error metrics.
Reporting & Documentation: Deliver a detailed report with methodology, assumptions, and the final forecasting model code along with documentation for future maintenance.
Skills and Experience Required:
Strong background in time series analysis and forecasting methods.
Proficiency in Python for data analysis and model development.
Experience with forecasting tools such as ARIMA, exponential smoothing, Prophet, or similar techniques.
Ability to interpret data insights and communicate results clearly.
Previous experience with similar forecasting projects is highly preferred.
Deliverables:
A clean and processed dataset ready for forecasting.
The forecasting model code (with comments) and detailed documentation.
A comprehensive report including data analysis, model selection rationale, performance metrics, and visualizations.
Recommendations for model deployment and future data monitoring
We are seeking an experienced data scientist or statistician to develop a forecasting model that predicts future trends based on our historical data. The goal is to build a robust and accurate model that can forecast incoming calls and workload over the coming 6 months. including identifying patterns, trends, and seasonality in our data.
Project Details:
Data Overview:
We have historical data spanning 2 years
Objectives:
Analyze and preprocess the historical data to identify trends, seasonality, and any anomalies.
Develop and test multiple forecasting models (e.g., ARIMA, exponential smoothing, Prophet, or machine learning approaches) to select the most accurate method.
Validate the model using appropriate metrics such as RMSE or MAPE.
Provide visualizations and a clear explanation of the forecasting results along with confidence intervals.
Scope of Work:
Data Preparation: Clean and preprocess the data to ensure quality input for the forecasting models.
Exploratory Analysis: Conduct a thorough analysis to understand trends, seasonality, and outliers.
Model Selection and Development: Experiment with different forecasting methods, tuning parameters to achieve optimal performance.
Validation: Compare model predictions against historical data and validate performance using standard error metrics.
Reporting & Documentation: Deliver a detailed report with methodology, assumptions, and the final forecasting model code along with documentation for future maintenance.
Skills and Experience Required:
Strong background in time series analysis and forecasting methods.
Proficiency in Python for data analysis and model development.
Experience with forecasting tools such as ARIMA, exponential smoothing, Prophet, or similar techniques.
Ability to interpret data insights and communicate results clearly.
Previous experience with similar forecasting projects is highly preferred.
Deliverables:
A clean and processed dataset ready for forecasting.
The forecasting model code (with comments) and detailed documentation.
A comprehensive report including data analysis, model selection rationale, performance metrics, and visualizations.
Recommendations for model deployment and future data monitoring