Automated Time Series Forecaster Selection
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Solution Overview
Problem
Conventional forecasting methods for time series data, especially with limited historical data, face challenges in selecting suitable models due to low capacity and require manual expert intervention, leading to impractical and inaccurate predictions.
Innovation Solution
An automated system that classifies time series data and selects the best-suited forecaster based on previous training, using a two-step process involving forecaster training and classification to improve prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual expert intervention is used to select forecasters, then model suitability can be improved, but forecasting efficiency and productivity deteriorate
Solution Approach 1:
The system performs self-service by automatically selecting forecasters based on time series characteristics without requiring manual expert intervention. The automated forecaster selection mechanism analyzes the time series data and autonomously determines the most suitable forecaster, eliminating the need for human experts while maintaining reliable model selection.
Solution Approach 2:
The manual mechanical process of expert selection is replaced with an automated computational system. The system uses algorithms and machine learning models to substitute the human expert's decision-making process, automatically matching forecasters to time series data based on learned patterns and characteristics.
2Quantity of substance
If low-capacity models with built-in models are used, then training data requirements are reduced, but model accuracy deteriorates due to mismatch between built-in model and actual time series behavior
Solution Approach 1:
The system changes the parameter of model capacity by using high-capacity forecasters that can be effectively trained even with limited data. Instead of being constrained by low-capacity models with fixed built-in assumptions, the system employs flexible high-capacity models whose parameters and structures can adapt to the specific characteristics of the time series data through automated selection and training.
Solution Approach 2:
The system introduces dynamics by allowing the forecaster selection and model configuration to adapt based on the specific characteristics of each time series. Rather than using static low-capacity models with fixed built-in assumptions, the system dynamically selects and configures forecasters that match the actual behavior patterns of the time series data.
3Reliability
If high-capacity deep neural network models are used, then prediction accuracy can be improved, but data availability requirements increase making them unsuitable for short time series
Solution Approach 1:
The system segments the forecasting problem by dividing it into multiple components: time series characteristic analysis, forecaster selection, and model training. This segmentation allows the system to use high-capacity forecasters effectively by first analyzing the specific characteristics of the time series data and then selecting the most appropriate forecaster, making high-capacity models suitable even for short time series through targeted application.
Solution Approach 2:
The system introduces an intermediary mechanism - the automated forecaster selection process - that bridges the gap between limited data availability and high-capacity model requirements. This intermediary analyzes the time series characteristics and selects forecasters that can achieve high accuracy with the available data, acting as a mediator that enables high-capacity models to work effectively with short time series.
4Device complexity
If conventional regression techniques are used, then simplicity is maintained, but adaptability to different time series types deteriorates
Solution Approach 1:
The system achieves universality by creating a multi-functional forecasting platform that can handle various types of time series data through automated forecaster selection. The system maintains simplicity at the user level while providing adaptability through its ability to automatically select from multiple forecaster types based on the specific characteristics of the input time series, making it universally applicable to different forecasting scenarios.
Data Source
AI summary
Systems and methods for forecasting time series data are provided. In one implementation, a method includes the steps of obtaining time series data from a network. The method also comprises the step of determining one or more forecasters to be used based on a type of the time series data and based on previous training that determine that the one or more forecasters from a number of forecasters are best suited for the type of time series data. The method further comprises making a forecast of the time series data using the one or more forecasters and to save and/or display the forecast.