@astermind/astermind-premium/dist/elm/time-series-elm.d.ts

Astermind Premium - Premium ML Toolkit

export interface TimeSeriesELMOptions {
    categories: string[];
    hiddenUnits?: number;
    sequenceLength?: number;
    lookbackWindow?: number;
    useRecurrent?: boolean;
    activation?: 'relu' | 'tanh' | 'sigmoid' | 'linear';
    maxLen?: number;
    useTokenizer?: boolean;
}
export interface TimeSeriesELMResult {
    label: string;
    prob: number;
    forecast?: number[];
}
/**
 * Time-Series ELM for temporal pattern recognition
 * Features:
 * - Temporal pattern recognition
 * - Optional recurrent connections
 * - Sequence-to-sequence prediction
 * - Forecasting capabilities
 */
export declare class TimeSeriesELM {
    private elm;
    private categories;
    private options;
    private history;
    private trained;
    constructor(options: TimeSeriesELMOptions);
    /**
     * Train on time-series data
     * @param X Sequences of features (each element is a time step)
     * @param y Labels for each sequence
     */
    train(X: number[][][], y: number[] | string[]): void;
    /**
     * Train on single sequences (convenience method)
     */
    trainSequences(sequences: number[][][], labels: number[] | string[]): void;
    /**
     * Predict from time-series sequence
     */
    predict(sequence: number[][] | number[][][], topK?: number): TimeSeriesELMResult[];
    /**
     * Forecast future values (for regression/forecasting tasks)
     */
    forecast(sequence: number[][], steps?: number): number[][];
    /**
     * Flatten sequences to feature vectors
     */
    private _flattenSequences;
    /**
     * Flatten a single sequence
     */
    private _flattenSequence;
    /**
     * Update history for recurrent mode
     */
    private _updateHistory;
    /**
     * Enhance features with history (recurrent mode)
     */
    private _enhanceWithHistory;
    /**
     * Clear history (useful for new sequences)
     */
    clearHistory(): void;
}
//# sourceMappingURL=time-series-elm.d.ts.map