Unleash the power of time series with Ikigai APIs

Unexpected events, outliers, or data errors can wreak havoc on your forecasts. Expose and remove disruptions from your data quickly for more reliable predictions.

• Improve forecast accuracy to ensure your team is focused on underlying trends, not abnormalities
• Gain operational insights to uncover potential issues in your data collection process, identify unexpected market shifts, or detect fraudulent activity

Markets evolve, customer behavior changes, and external factors can cause sudden shifts in your time series data. Change point detection helps you quickly adapt for more accurate, informed forecasts.

• Respond to dynamic environments by identifying when significant changes occur so you can adjust your strategies and forecasts accordingly
• Understand key drivers by investigating the factors that contribute to changing business outcomes
• Gain valuable insights into your business and the market

Time series data is made up of trends, seasonality, and residual noise. Decomposition separates those elements for clearer insights into forecast drivers.

• Isolate key trends to uncover the long-term direction of your data, unobscured by short term fluctuations
• Understand seasonal patterns by identifying cycles or patterns, such as weekly, monthly, or yearly fluctuations
• Improve forecast interpretability to gain a clearer understanding of how different factors contribute to your overall forecast

Prepare your time series data for machine learning with robust Time2Vec implementation, ensuring that data is ready for any machine learning models.

• Perform cohort analysis to group similar products or customer segments based on their historical behavior, enabling targeted marketing and personalize experiences
• Discover unique groupings, patterns and relationships such as similar products or customer segments based on their historical behavior, enabling targeted strategies and actions

Get a clear view into how forecasts are generated, fostering transparency and trust.

• Evaluate model performance with key model metrics like MAPE (Mean Absolute Percentage Error), ensuring your forecasts are on the right track
• Decompose your forecasts into their consistuent components, such as trends and seasonality, to understand the factors driving your predictions