/**
 * Predictive Spend Forecaster for x402-cfo.
 *
 * Uses online linear regression on spend timestamps to forecast:
 *   - Time until budget exhaustion (per agent or pool)
 *   - Spend rate ($/minute, $/hour)
 *   - Confidence interval based on spend variance
 *
 * Based on ad pacing research: optimal spend is proportional to request
 * rate, not linear over time. The forecaster detects whether spend is
 * accelerating, decelerating, or steady.
 *
 * Uses online regression (no stored history) for O(1) memory.
 * Sufficient statistics: n, sum_x, sum_y, sum_xy, sum_x2
 * where x = time offset (seconds), y = cumulative spend.
 */
/** Forecast result. */
export interface SpendForecast {
    /** Current spend rate ($/second) */
    ratePerSecond: number;
    /** Current spend rate ($/minute) */
    ratePerMinute: number;
    /** Current spend rate ($/hour) */
    ratePerHour: number;
    /** Estimated time until budget exhaustion (ms). Infinity if rate <= 0 or no budget set. */
    exhaustionEtaMs: number;
    /** Estimated timestamp when budget will be exhausted. null if not applicable. */
    exhaustionAt: Date | null;
    /** Spend trend: 'accelerating' | 'steady' | 'decelerating' */
    trend: 'accelerating' | 'steady' | 'decelerating';
    /** R² goodness-of-fit for the linear model (0-1). Higher = more predictable spend. */
    confidence: number;
    /** Total spend so far */
    totalSpent: number;
    /** Budget remaining. null if no budget set. */
    remaining: number | null;
    /** Number of observations */
    observations: number;
}
export interface ForecasterConfig {
    /** Total budget to forecast exhaustion against. */
    budget?: number;
    /** Minimum observations before forecasting. Default: 3 */
    minObservations?: number;
}
/**
 * Online linear regression forecaster for spend pacing.
 *
 * Tracks cumulative spend over time and fits y = mx + b where:
 *   x = time offset from first observation (seconds)
 *   y = cumulative spend ($)
 *   m = spend rate ($/second)
 *
 * Detects acceleration by comparing recent rate to overall rate.
 */
export declare class SpendForecaster {
    private budget;
    private minObservations;
    private n;
    private sumX;
    private sumY;
    private sumXY;
    private sumX2;
    private sumY2;
    private firstTimestamp;
    private lastTimestamp;
    private totalSpent;
    private recentRates;
    private prevSpend;
    private prevTimestamp;
    constructor(config?: ForecasterConfig);
    /**
     * Record a spend observation.
     *
     * @param amount - Amount spent in this transaction
     * @param timestamp - When it occurred (ms). Default: now.
     */
    observe(amount: number, timestamp?: number): void;
    /**
     * Get the current spend forecast.
     * Returns null if insufficient observations.
     */
    forecast(now?: number): SpendForecast | null;
    /** Update the budget target. */
    setBudget(budget: number): void;
}
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