ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning

Ziyue Yang; Chaolin Xu; Yijing Wang; Tiankai Gu; Hui Yang; Yanhong Lin; Kaiyuan Liu; Fei Xiao

cs.AIAug 15, 2026

ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning

Authors: Ziyue Yang, Chaolin Xu, Yijing Wang, Tiankai Gu, Hui Yang, Yanhong Lin, Kaiyuan Liu, Fei Xiao

Organizations: Taobao and Tmall Group, Alibaba Group China

Abstract

Demand forecasting increasingly requires combining two complementary sources of information: historical sales reveal recurring numerical dynamics, while future promotions, holidays, price changes, and platform interventions provide forward-looking knowledge. Existing text-enhanced forecasting methods often encode such context into generic representations and fuse it uniformly with time-series features, without explicitly distinguishing which semantic effects are forecast-relevant or how they should modify future dynamics. We introduce ReasonCast, a structured semantic intervention framework that translates event knowledge into forecast-specific operations. An agent examines the event context, the no-text forecast, and its uncertainty to determine whether textual reasoning is needed. Rather than injecting free-form text, ReasonCast represents event knowledge through structured fields describing event relevance, demand direction, temporal shape, amplitude, and peak intensity. These fields interact selectively with temporal components of a time-series foundation model. An additive path corrects local trends and temporal shapes, while a multiplicative path captures event-driven level shifts. ReasonCast introduces a forecast-grounded post-training curriculum. Schema SFT establishes semantic fields; semantic-field RL calibrates direction, shape, amplitude, and peak judgments; and forecast-utility RL evaluates semantic interventions through a frozen forecaster, aligning reasoning outputs with marginal forecast improvement. ReasonCast lowers WMAPE by 3.29, 1.25, and 0.47 percentage points on holiday-sensitive categories, mega-sale-sensitive categories, and M5 event windows, respectively. On stable-sales periods, indiscriminate semantic intervention increases WMAPE by 1.68 percentage points, whereas suppressing unnecessary intervention preserves the numerical backbone.

Figures & tables

Forecasting design	Numerical forecast owner	Reasoning over exogenous events	Selective semantic intervention	Exact no-text preservation	Reasoner adaptation
TS-only / TSFM ( Ansari et al., 2024 ; Das et al., 2024 )	TSFM	$\times$	$\times$	–	N/A
LLM numerical forecasting ( Gruver et al., 2023 ; Jin et al., 2024 )	LLM	$\times$	$\times$	$\times$	Frozen / prompting
Joint multimodal forecasting ( Jia et al., 2024 ; Li et al., 2026 ; Wang et al., 2025 ; Wu et al., 2026 ; Zhou et al., 2025 )	Joint model	$\triangle$	$\times$	$\times$	Task-level training / SFT
From News to Forecast ( Wang et al., 2024b )	LLM	$\checkmark$	$\times$	$\times$	Reflection + evidence reselection
VoT ( Wang et al., 2026 )	Hybrid branches	$\checkmark$	$\triangle$	$\times$	Retrieval + historical ICL
ReasonCast	TSFM	$\checkmark$	$\checkmark$	$\checkmark$	SFT + semantic-field RL + forecast-utility RL

Table 1. Comparison of representative forecasting designs.

\checkmark

\triangle

, and

\times

denote explicit, partial or method-dependent, and no explicit support. Reasoning over exogenous events goes beyond encoding external text to infer forecast-relevant effects. Selective semantic intervention combines instance-level Skip / Basic / Tool routing with continuous control over intervention strength; designs without an explicit abstention route provide only partial support. Reasoner adaptation covers both parametric post-training and non-parametric use of forecasting feedback.

Figure 1. Overview of ReasonCast. (a) ReasonCast follows a reason–align–intervene–forecast pipeline. An evidence-augmented LLM agent combines the historical series with temporal statistics, promotion intelligence, platform signals, and external signals to produce structured event semantics. A text encoder and an MLP aligner map these semantics to aligned tokens $\mathbf{T}$ . In parallel, a pretrained time-series foundation model (TSFM) produces a future token $\mathbf{h}_{F}$ , which is orthogonally decomposed at the fusion interface into event-related and complementary components $\mathbf{h}_{E}$ and $\mathbf{h}_{N}$ . Only $\mathbf{h}_{E}$ queries $\mathbf{T}$ through event-selective cross-attention. An adaptive semantic gate modulates the additive latent correction for local trend and shape changes, while a complementary multiplicative path handles large event-driven level shifts. Without semantic context, both correction paths are disabled and the fusion interface exactly recovers the original TSFM computation. (b) The training curriculum comprises schema SFT, semantic-field RL, orthogonal event-subspace grounding and selective fusion training, and forecast-utility RL. During forecast-utility RL, candidate semantics are compared with the cached semantic-field-policy fusion result, while degradation beyond the no-text TSFM is explicitly penalized. A two-panel overview. The first panel traces event evidence through an LLM reasoner, text alignment, event-selective fusion, and a time-series forecast. The second panel shows the SFT, semantic-field RL, fusion training, and forecast-utility RL curriculum.

Method	Holiday						Mega-sale						M5 event windows
	Sens.		Insens.		All		Sens.		Insens.		All		WMAPE	MAE
Seasonal Naive	119.96%	2738.47	56.69%	609.09	47.39%	571.90	27.94%	766.85	12.49%	283.57	16.55%	434.69	44.55%	5.47
LightGBM	74.61%	1671.02	43.20%	489.11	34.23%	447.08	26.94%	732.37	12.83%	290.49	16.23%	425.58	34.06%	4.17
PatchTST	84.90%	1771.26	47.80%	516.75	39.78%	485.47	22.94%	652.38	9.91%	214.34	13.41%	355.74	48.31%	5.91
iTransformer	79.29%	1849.54	42.03%	486.83	33.64%	450.65	23.07%	653.62	11.30%	248.18	14.49%	382.73	34.39%	4.21
TimesFM	67.23%	1586.46	48.82%	557.09	39.37%	517.45	24.20%	701.77	14.57%	316.56	17.63%	465.66	34.63%	4.23

Table 2. Main forecasting results on event-centered evaluations. Cells report WMAPE/MAE; lower values are better. Sens. and Insens. denote event-sensitive and event-insensitive leaf categories, respectively. The best results are highlighted in bold, and the second-best results are underlined.

Method	WMAPE	MAE
Seasonal Naive	10.89%	254.82
LightGBM	11.10%	259.88
PatchTST	9.72%	227.22
iTransformer	10.26%	239.83
TimesFM	13.19%	308.73
Chronos1	8.33%	194.61

Table 3. Forecasting performance during stable-sales periods on the proprietary commerce dataset. Lower values are better. The best results are highlighted in bold, and the second-best results are underlined.

Design axis	Variant	Holiday Sens.	Mega-sale Sens.	Stable-sales (forced Basic)	M5
Full fusion mechanism		39.10% / 1080.71 [-1pt] reference	20.89% / 589.94 [-1pt] reference	9.59% / 224.43 [-1pt] reference	32.63% / 4.00 [-1pt] reference
Interaction target	w/o orthogonal decomposition–alignment	40.04% / 1155.23 [-1pt] $\Delta\;+0.94\,\mathrm{pp}\,/\,+6.90\%$	21.14% / 596.19 [-1pt] $\Delta\;+0.25\,\mathrm{pp}\,/\,+1.06\%$	11.31% / 264.57 [-1pt] $\Delta\;+1.72\,\mathrm{pp}\,/\,+17.89\%$	32.64% / 4.00 [-1pt] $\Delta\;+0.01\,\mathrm{pp}\,/\,+0.00\%$
Intervention strength	w/o adaptive semantic gate	39.76% / 1112.21 [-1pt] $\Delta\;+0.66\,\mathrm{pp}\,/\,+2.91\%$	21.54% / 608.01 [-1pt] $\Delta\;+0.65\,\mathrm{pp}\,/\,+3.06\%$	9.64% / 225.53 [-1pt] $\Delta\;+0.05\,\mathrm{pp}\,/\,+0.49\%$	32.64% / 4.01 [-1pt] $\Delta\;+0.01\,\mathrm{pp}\,/\,+0.25\%$
Correction form	w/o additive correction	40.12% / 1189.98 [-1pt] $\Delta\;+1.02\,\mathrm{pp}\,/\,+10.11\%$	22.14% / 622.09 [-1pt] $\Delta\;+1.25\,\mathrm{pp}\,/\,+5.45\%$	8.85% / 206.99 [-1pt] $\Delta\;-0.74\,\mathrm{pp}\,/\,-7.77\%$	32.84% / 4.03 [-1pt] $\Delta\;+0.21\,\mathrm{pp}\,/\,+0.75\%$
	w/o multiplicative correction	44.29% / 1159.19 [-1pt] $\Delta\;+5.19\,\mathrm{pp}\,/\,+7.26\%$	20.89% / 589.95 [-1pt] $\Delta\;+0.00\,\mathrm{pp}\,/\,+0.00\%$	9.59% / 224.43 [-1pt] $\Delta\;+0.00\,\mathrm{pp}\,/\,+0.00\%$	32.65% / 4.01 [-1pt] $\Delta\;+0.02\,\mathrm{pp}\,/\,+0.25\%$
Reference	Base fusion only	44.30% / 1229.05 [-1pt] $\Delta\;+5.20\,\mathrm{pp}\,/\,+13.73\%$	21.50% / 606.18 [-1pt] $\Delta\;+0.61\,\mathrm{pp}\,/\,+2.75\%$	8.74% / 204.43 [-1pt] $\Delta\;-0.85\,\mathrm{pp}\,/\,-8.91\%$	33.03% / 4.04 [-1pt] $\Delta\;+0.40\,\mathrm{pp}\,/\,+1.00\%$

Table 4. Selective-fusion ablations (WMAPE/MAE). The stable-sales column forces Basic to isolate fusion from routing. Gray values report changes from the full fusion mechanism as WMAPE percentage points / relative MAE (%); positive values indicate degradation. Lower is better.

Training stage	Parse Rate $\uparrow$	Direction MAE $\downarrow$	Shape Weighted-F1 $\uparrow$	Amplitude log-MAE $\downarrow$	Peak log-MAE $\downarrow$	Event WMAPE $\downarrow$	Negative Transfer Rate $\downarrow$
Original LLM	91.36%	1.14	0.25	0.44	0.54	20.52%	23.59%
+ Schema SFT	100.00%	0.91	0.34	0.44	0.53	19.48%	16.20%
+ Semantic-Field RL (GRPO)	100.00%	0.85	0.33	0.39	0.52	19.16%	16.03%
+ Forecast-Utility RL (GRPO)	100.00%	0.83	0.35	0.38	0.53	18.96%	15.08%

Table 5. Semantic fidelity and downstream forecast utility across cumulative reasoner post-training stages. Event WMAPE pools holiday-sensitive and mega-sale-sensitive observations. Negative Transfer Rate is the proportion of instances for which text-conditioned seven-day normalized absolute error exceeds the exact no-text forecast from the same frozen forecaster. Arrows indicate whether higher or lower values are better.

Figure 2. Entropy-adaptive semantic intervention across within-window forecast-entropy tertiles. (a) WMAPE of the no-text backbone; (b) mean additive-injection magnitude

|g_{i}|

, with the dashed line denoting the global-

\alpha

control; and (c) WMAPE gain over that control in percentage points. Three panels group forecasts into low-, medium-, and high-entropy tertiles, showing increasing no-text backbone WMAPE, increasing additive-injection magnitude, and positive WMAPE gains over a global-

\alpha

control.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

	SNaive		LightGBM		PatchTST		iTransformer		TimesFM		C2-small
Window	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE
2025-11-11	19.61%	547.89	18.44%	515.27	19.68%	550.02	19.63%	548.61	23.87%	667.14	18.03%	509.35
2025-12-08	15.55%	392.93	17.21%	434.92	10.72%	270.99	12.40%	313.53	15.12%	382.17	15.94%	406.74
2025-12-11	14.51%	363.26	13.04%	326.56	9.84%	246.23	11.43%	286.05	13.89%	347.66	9.94%	251.24
2026-01-01	18.68%	446.77	17.61%	421.21	18.82%	450.14	18.36%	439.06	19.72%	471.68	29.83%	719.76
2026-02-12	210.75%	1614.50	169.72%	1300.13	175.83%	1347.00	159.53%	1222.10	186.35%	1427.55	138.84%	1070.39

Appendix

Table 6. Commerce window-level results, baseline group A. Each method occupies a WMAPE (%) and MAE pair. Rows 1–3 are mega-sale windows, rows 4–13 are holiday windows, and rows 14–15 are stable-sales windows.

	TS backb.		+ covariates		Chronos		Time-LLM		VoT		ReasonCast
Window	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE
2025-11-11	18.04%	504.26	17.74%	495.69	17.67%	493.82	20.11%	561.93	22.86%	638.72	17.18%	480.21
2025-12-08	13.31%	336.44	11.61%	293.52	16.84%	425.55	16.42%	415.09	15.13%	382.42	11.25%	284.27
2025-12-11	12.30%	307.93	10.69%	267.51	10.20%	255.44	14.38%	359.93	12.51%	313.13	10.17%	254.56
2026-01-01	24.92%	596.10	27.36%	654.40	27.72%	663.06	15.69%	375.23	19.89%	475.70	22.29%	533.21
2026-02-12	131.66%	1008.59	116.26%	890.61	141.52%	1084.17	210.34%	1611.33	108.75%	833.09	83.85%	642.36

Appendix

Table 7. Commerce window-level results, baseline group B and ReasonCast. Each method occupies a WMAPE (%) and MAE pair.

	SNaive		LightGBM		PatchTST		iTransformer		TimesFM		C2-small
Event and origin	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE
Halloween 10-25	37.20%	4.55	29.25%	3.58	45.65%	5.59	29.45%	3.61	29.84%	3.65	29.29%	3.59
Halloween 10-31	38.36%	4.86	30.46%	3.86	44.79%	5.68	30.71%	3.89	30.17%	3.82	29.38%	3.72
Thanksgiving 11-21	46.84%	5.45	35.41%	4.12	53.78%	6.26	36.71%	4.27	37.91%	4.41	36.64%	4.27
Thanksgiving 11-24	50.78%	5.46	37.95%	4.08	56.76%	6.11	41.32%	4.44	41.86%	4.50	41.30%	4.44
Christmas 12-21	59.59%	6.17	49.74%	5.15	68.18%	7.06	49.61%	5.13	50.30%	5.21	49.69%	5.14

Appendix

Table 8. M5 window-level results, baseline group A. Each method occupies a WMAPE (%) and MAE pair.

	TS backb.		+ covariates		Chronos		Time-LLM		VoT		ReasonCast
Event and origin	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE	WMAPE	MAE
Halloween 10-25	29.59%	3.62	29.24%	3.58	30.50%	3.73	35.20%	4.31	33.53%	4.11	28.83%	3.53
Halloween 10-31	30.52%	3.87	29.76%	3.77	30.55%	3.87	35.21%	4.46	35.73%	4.53	29.52%	3.74
Thanksgiving 11-21	33.77%	3.93	33.46%	3.89	37.69%	4.39	42.81%	4.98	38.81%	4.52	33.81%	3.94
Thanksgiving 11-24	35.85%	3.86	36.40%	3.92	41.53%	4.47	45.59%	4.90	39.83%	4.28	35.71%	3.84
Christmas 12-21	47.10%	4.87	47.72%	4.94	50.25%	5.20	54.21%	5.61	53.72%	5.56	48.34%	5.00

Appendix

Table 9. M5 window-level results, baseline group B and ReasonCast. Each method occupies a WMAPE (%) and MAE pair.

Event	Window	Correct WMAPE	Inverted WMAPE	$\Delta$	Correct MAE	Inverted MAE
520	2026-05-16	7.38%	7.77%	0.39	252.36	265.70
520	2026-05-19	5.78%	6.47%	0.69	218.79	244.81
Double 11	2025-11-05	13.27%	14.03%	0.76	154.35	163.25
Double 11	2025-11-08	17.54%	19.15%	1.61	959.36	1047.42
Double 11	2025-11-11	13.76%	13.62%	-0.15	618.88	612.33
Double 12	2025-12-08	9.89%	11.09%	1.19	163.66	183.39

Appendix

Table 10. Semantic inversion by window.

\Delta

is inverted minus correct WMAPE in percentage points.

Field	Value space and forecasting role
Intervention action	Skip, basic, or tool. Controls whether semantic generation is bypassed, uses available context, or invokes additional evidence/statistics.
Relevance	Related / unrelated. Determines whether the context is a plausible intervention rather than incidental calendar text.
Dominant event	Event name / none. Resolves overlapping holidays and promotions to a primary driver.
Direction	Large decrease, small decrease, flat, small increase, or large increase; describes the seven-day mean relative to the recent mean.
Temporal shape	Increasing, decreasing, rise–fall, fall–rise, or flat; specifies the within-horizon trajectory and turning behavior.
Amplitude	Positive scalar; ratio of the predicted seven-day mean to the recent seven-day mean.

Appendix

Table 11. ReasonCast reasoner output schema.

Compressed rationale (English translation)
The window covers the March 8 platform campaign on day 2. Laptops are not a core Women’s Day gift category, but they respond to platform-wide promotions. Recent demand is already elevated, so the additional effect should be moderate rather than comparable with Double 11 or 618. We therefore expect a short peak of about $1.4\times$ on March 8 followed by a return toward the recent level, producing a seven-day mean of about $1.2\times$ .
Structured target
Relevance: related Dominant event: Women’s Day Direction: small increase Shape: rise–fall Amplitude: 1.2 Peak: 1.4 Trend: promotion-driven peak on March 8, followed by a return toward the recent level.

Appendix

Table 12. Representative compressed rationale and structured output for a laptop forecast from March 7 to March 13, with Women’s Day on horizon day 2.

Component	Configuration
Student model	Qwen3-32B ( Yang et al., 2025 ) for SFT and both GRPO stages.
SFT data	4,590 compressed teacher examples; 2% validation split.
SFT adapter	LoRA on all linear layers, rank 16, scale 32, dropout 0.05.
SFT optimization	Three epochs; learning rate $1\times 10^{-4}$ ; cosine schedule; warmup ratio 0.03; BF16; effective batch size 32 (eight workers, batch size 1, four accumulation steps).
Semantic-field adapter	New LoRA initialized on the SFT-merged model; rank 8, scale 16, dropout 0; all linear layers.
Semantic-field sampling	Group size 8; temperature 0.8; top- $p$ 0.95; top- $k$ 50; maximum prompt/completion lengths 2816/512 tokens.

Appendix

Table 13. SFT and semantic-field RL configuration.

Component	Configuration
Forecast-utility data and sampling	5,500 prompts, one epoch (1,834 updates); group size 8; the same maximum prompt/completion lengths and rollout sampling as semantic-field RL.
Forecast-utility optimization	Learning rate $2\times 10^{-6}$ ; cosine schedule; warmup ratio 0.03; global KL coefficient 0.02 against the frozen semantic-field policy, plus a prompt-conditional SFT KL coefficient in $[0,0.004]$ .
Forecast-utility routing and gate	Sample skip/basic/tool with each rollout; disallow skip for holidays and mega-sales. Skip bypasses field parsing and uses the cached no-text forecast. For non-skip routes, require field reward $A_{i}\geq\max(A_{i}^{\mathrm{ref}}-0.08,0.10)$ and reject hard cross-field contradictions before invoking the frozen fusion model.
Forecast utility	Compare candidate MAE with the cached semantic-field-policy fusion MAE and the no-text TSFM MAE; penalize harm beyond $1.05$ times the no-text MAE. Utility is clipped on a 0.25 relative-error scale. Its weight is 0.5 in the reference run and 1.5 after the fixed step-500 diagnostic; a normalized route-cost penalty makes tool use more expensive than basic reasoning and assigns zero cost to skip.

Appendix

Table 14. Forecast-utility RL (GRPO) configuration.

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CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang+5

State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China

Time Series Forecasting LLM-Based Time Series Forecasting Retrieval-Augmented Time Series Forecasting

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Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features. Recent advances in large language models (LLMs) have extended forecasting beyond numerical extrapolation toward context-aware reasoning. However, existing approaches often lack explicit mechanisms to identify relevant contexts, reason about their impacts, and validate forecasts against temporal and domain constraints. In this work, we propose CastFSR, an agentic framework that formulates context-aware forecasting as a Fast--Slow--Reflect workflow. In fast thinking, CastFSR profiles observations and selects lightweight forecasters to construct a data-driven forecast prior. In slow deliberation, it retrieves contextual evidence, adaptively determines informative look-back windows, and reasons about how contexts reshape future dynamics. In reflection, it iteratively refines forecasts to ensure temporal, contextual, and domain consistency. CastFSR supports both training-free inference with off-the-shelf LLMs and efficient deployment through a two-stage SFT and reinforcement learning strategy that transfers its orchestration capability to compact LLMs. Extensive experiments on public datasets demonstrate that CastFSR consistently outperforms representative baselines. Our code is available at https://github.com/Xiaoyu-Tao/CastFSR.

Abstract

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