Any model is only as good as the data feeding into it. With robust research methodologies, advanced extraction tools, and rigorously selected third-party providers, our datasets are some of the most accurate on the market. All of our tools employ leading data science techniques that are meticulously tested to ensure the highest levels of accuracy. This means all predictions are fact-based.
Our key step in building a solid model is incorporating Kline’s domain knowledge. Unbiased outputs based on machine findings are refined using our years of industry knowledge to determine the most effective methodology. This approach means you have the tools to beat the competition and to identify and take advantage of new trends and opportunities ahead of your peers.
Advanced Data Science, Robust Predictions
Kline applies strict processes and advanced data science techniques not only in its forecasting tool but across the suite of its predictive analytics products, including multiple linear regression, Bayesian analysis, natural language processing, decision trees, and clustering. Kline utilizes an end-to-end analytics platform, powered by Python programming software, resulting in highly robust predictions.
- Multiple linear regression: The relationship between each parameter and the market data is analyzed to build a series of multiple linear regression equations that represent the market trajectory. Both linear and nonlinear terms (for example, logarithmic and power terms) are considered here.
- Bayesian analysis: The multiple linear regression equations are then recurred using a Bayesian iterator to develop a model that fits the data with the greatest degree of accuracy. This equation is used to determine future market forecasts.
- Scenario calculations: The multiple linear regression equation is used to calculate the change in market data, based on changes made to any of the factors. This provides you with the capability to input contrasting future scenarios and deciphers the effect of these events on your products and the overall market.
Forecast veracity is scrutinized extensively. All predictive models are tested from both theoretical and practical viewpoints to ensure that the predictions are as robust as possible, with results feeding back into algorithms in a closed learning loop.
- Theoretical error: Statistical techniques are applied to decipher the theoretical error terms of the predicted data, with continual refinement to reach high levels of accuracy.
- Real-world comparison: Historical predictions are compared against observed data and short-term future predictions, as well as measured against industry expert expectations, to test their real-world success.
- Quality control: Quality checks, including trend analysis and cross-country validation, are undertaken by a dedicated team. Feedback is provided to the domain experts for further enhancement.
- Industry expertise: Predictions are further reviewed and refined by industry experts, considering hard-to-predict events, such as legislation changes or key company marketing campaigns.
- Scenario planning: Revising future parameter values provides updated market forecasts based on correlated relationships.