TIMBRE: Teaching Time Series Forecasters to Read, Remember, and Reconcile

Xinyu Guan; Zhirong Zhang; Hongyuan Liu; Pengcheng Xu; Yu Sun; Chen Song; Qianyang Zhao

cs.LGOct 3, 2026

TIMBRE: Teaching Time Series Forecasters to Read, Remember, and Reconcile

Authors: Xinyu Guan, Zhirong Zhang, Hongyuan Liu, Pengcheng Xu, Yu Sun, Chen Song, Qianyang Zhao

Organizations: Alibaba Group · Chang’an University · University of Electronic Science and Technology of China · University of Bristol ·

Abstract

Event-informed forecasting requires translating reports and historical responses into changes to a numerical forecast. We propose TIMBRE (Temporal Integration of Memory-Based Responses and Evidence), which combines source-aware representation, state-conditioned response transfer, and reliability-guided fusion before a frozen forecast head. A separate readout adjusts interval widths while preserving the median. In a single-seed, one-epoch development study of 13 tasks, TIMBRE improves MAE over ordinary fusion on eight tasks but over native Chronos-2 on only two. Disabling response transfer in the trained model reduces BTC and AULF MAE by 47.04% and 6.81%, respectively. These findings identify sensitivity to learned response transfer rather than a general forecasting advantage. Missing development-set scores and the absence of retrained ablations limit attribution to individual evidence mechanisms.

Figures & tables

Figure 1: Motivating BTC example. News and numerical history jointly inform a forecast adjustment. Target and forecast positions are schematic, not measured results.

Figure 2: TIMBRE’s numerical and event-memory paths. Source relations (A), response transfer (B), and reliability (C) inform horizon-aware fusion (F), updating future patch states before the native head. C controls median-preserving interval readout after shared postprocessing. Numerical conditioning of B/C/F is omitted; curves and weights are schematic.

Data family	FIT	TUNE	$H$	$M^{+}$
BTC	1,999	128	6	128
AULF	2,805	705	48	705
NHC	4,999	807	40	807
Time-MMD (10)	3,990	599	6	588
Total	13,793	2,239	–	2,228

Table 1: Prepared development data. FIT counts follow boundary purging;

H

is the output-grid length.

M^{+}

counts TUNE windows with at least one supported memory point, not full-horizon coverage. NHC uses a masked grid.

Time-MMD
Task	$n$	History mean	Persistence	Seasonal	Chronos-2	Ordinary	TIMBRE
AULF	705	473.04	469.12	270.08	245.52	250.31	268.80
BTC	128	1682.16	311.43	443.23	322.75	428.72	649.24
NHC	807	14.121	12.387	12.387	13.184	13.569	14.447
Agriculture	64	9.508	3.240	5.952	3.938	4.003	3.999
Climate	64	11.005	4.089	16.006	4.033	4.240	4.187

Table 2: MAE on the common TUNE panel; lower is better. BTC is in USD, AULF in MW, and NHC in knots. Time-MMD rows retain source units; Security values are divided by

10^{6}

for display.

n

counts windows. Bold highlights TIMBRE; underlining marks the lowest value among six methods, using unrounded results. History mean and seasonal naive are exploratory controls added after the trained results were seen.

	BTC		AULF
Method	PB	C90 (%)	PB	C90 (%)
Native Chronos-2	118.27	91.02	87.05	83.53
Ordinary fusion	168.10	97.27	88.78	87.09
TIMBRE	239.18	84.24	96.14	81.67

Table 3: Probabilistic TUNE results. PB is mean pinball loss (lower is better); C90 is coverage of the nominal 90% interval. WIS is

2\times

PB on the shared grid.

	BTC		AULF
Inference mode	MAE	PB	MAE	PB
TIMBRE	649.24	239.18	268.80	96.14
w/o A: source	483.14	178.66	263.62	94.04
w/o B: transfer	343.86	126.60	250.51	88.79
w/o C: reliability	731.43	268.70	272.68	97.65

Table 4: Ablation by inference-time removal with the same trained TIMBRE weights, seed 17. All 833 BTC/AULF windows are retained; zero training updates. These are fixed-weight diagnostics, not retrained ablations.

Figure 3: Fixed-checkpoint diagnostics on 833 BTC/AULF TUNE windows: (a) module removal; (b) native–full output interpolation, native MAE = 100; (c) input-content masks retaining availability metadata. MAE changes are relative to FULL. Seed 17; no retraining, setting selection, or across-seed error bars.

Time Series Forecasting

Time series forecasting predicts future values from ordered observations using statistical, neural, and probabilistic models to support decisions under uncertainty.

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