ARIMA, Prophet, and Holt-Winters models with automatic model selection
Time-series forecasting predicts future values based on historical patterns over time. In actuarial work, this is essential for forecasting claim volumes, premium trends, reserve requirements, and financial metrics.
Instead of just looking at static snapshots, time-series analysis understands how things change over time—capturing trends, seasonal patterns, and cyclical behaviors to make accurate predictions.
What it is: A classical statistical model that uses past values and past errors to predict future values
How it works: Combines three components: AR (uses past values), I (removes trends), and MA (uses past forecast errors). It's like learning from both what happened and what you got wrong before.
Best for: Data with clear trends and patterns. Works well for monthly/quarterly business metrics like claim counts or premium revenue.
Example: Forecasting next quarter's claim volume based on the past 3 years of monthly claims, accounting for seasonal spikes.
What it is: A modern forecasting tool designed for business time series with strong seasonal patterns
How it works: Decomposes your data into trend + seasonality + holidays. Automatically detects weekly, monthly, and yearly patterns. Very robust to missing data.
Best for: Business data with multiple seasonal patterns (daily, weekly, yearly). Handles holidays and special events automatically.
Example: Predicting daily website traffic for an insurance portal, accounting for weekday/weekend patterns and holiday effects.
What it is: A smoothing technique that gives more weight to recent observations
How it works: Uses exponentially decreasing weights for older data. Captures level, trend, and seasonal components. Simple but effective.
Best for: Data with clear seasonal patterns and trends. Fast to compute and easy to interpret. Great for short-term forecasts.
Example: Forecasting next month's claim costs when you have consistent seasonal patterns (e.g., higher claims in winter).
Provide data with a date/time column and a value column (e.g., monthly claims, daily premiums). ADaaS automatically detects the time frequency.
ADaaS analyzes your data for trends (upward/downward), seasonality (repeating patterns), and stationarity (stability over time).
ARIMA, Prophet, and Holt-Winters models are all trained on your data. Each model makes predictions for a test period.
ADaaS compares models using MAPE (Mean Absolute Percentage Error). Lower is better. The best model is automatically selected for your forecast.
Get point forecasts plus upper/lower bounds showing the range of likely outcomes. For example: "Next month: 1,200 claims (range: 1,050-1,350)."
See how each model performed on historical data. Compare MAPE scores to understand which model is most accurate for your specific data.
Visualize your data broken down into trend, seasonal, and residual components. Understand what drives your time series.
Zoom, pan, and explore your historical data and future forecasts. Export charts for presentations and reports.
Predict future claim counts to plan staffing, set reserves, and manage cash flow. Essential for operational planning.
Forecast premium income for budgeting and financial planning. Understand seasonal patterns in policy sales.
Track and predict loss ratios over time. Identify deteriorating trends early to adjust pricing or underwriting.
Project future reserve requirements based on historical claim development patterns and trends.
Upload your time-series data and get instant forecasts