SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

Mingtian Tan; Palash Goyal; Mihir Parmar; Sarkar Snigdha Sarathi Das; Chun-Liang Li; Nanyun Peng; Thomas Hartvigsen; Jinsung Yoon

cs.LGOct 2, 2026

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

Authors: Mingtian Tan, Palash Goyal, Mihir Parmar, Sarkar Snigdha Sarathi Das, Chun-Liang Li, Nanyun Peng, Thomas Hartvigsen, Jinsung Yoon, +1 more

Organizations: Google Cloud AI Research · University of Virginia

Abstract

Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retrieve external news, standard retrieval-augmented approaches struggle with high noise, missing signals, and an inability to reason causally about event impacts. We propose SEER (Self-Evolving Event Reasoning and Retrieval), a closed-loop framework that dynamically optimizes event conditioning for time series forecasting. SEER translates prediction errors into two decoupled feedback mechanisms: (i) a reflective retrieval memory that refines subsequent search queries and filters spurious noise, and (ii) a persistent causal knowledge base that distills transferable domain dynamics. SEER enforces strict chronological boundaries across both event retrieval and reflection, preventing look-ahead bias and data leakage. Across six volatile time-series benchmarks, SEER consistently outperforms state-of-the-art time series foundation models and language model baselines.

Figures & tables

Figure 1: Performance and interpretability of SEER. (a) SEER demonstrates consistent performance improvements over prior forecasting paradigms across event-driven time series tasks (e.g., Memory/SSD prices, Electricity, and Weather). Detailed results and comparisons with additional baselines are provided in Table 2 (§ 4.3 ). (b) A memory price forecasting case study ( 64GB-DDR5 from Pangoly (2026) ) illustrates SEER’s interpretable predictions, driven by dynamic event retrieval and causal knowledge discovery. For details, see Figure 5 and 6 in Appendix B .

Figure 2: Overview of the SEER framework. At step

t

, an LLM Forecaster (

f

) generates a reasoning process

r_{t}

, which contains the prediction

\hat{y}_{t}

, based on historical numerical data

x_{t}

and a dynamic textual context space. This context space supplies the LLM with augmented external events (further retrieved using

\mathcal{M}_{t}^{\text{ret}}

and filtered via reflective memory

\mathcal{M}_{t}^{\text{sel}}

) alongside persistent causal knowledge

\mathcal{K}_{t}

. Upon observing the true outcome

y_{t}

, SEER closes the feedback loop by reflecting on the prediction error. The updates are decoupled:

\Phi_{\mathrm{mem}}

refines the Event-Augmented Memory

\mathcal{M}_{t+1}

, while

\Phi_{\mathrm{know}}

extracts transferable domain dynamics from past errors to form

\mathcal{K}_{t+1}

Domain	Entities	Learning Window	Evaluation Window	Horizon	Target (Metric)
Weather	10	3 seasonal ranges †	2026-02-22 – 2026-07-19	7 days	Daily Temp (MAE, MSE)
Electricity	3	3 seasonal ranges †	2026-02-22 – 2026-07-19	7 days	Daily Demand (MAE, MSE)
Stock	50	2025-10-01 – 2026-02-01	2026-02-01 – 2026-05-01	1 day	Daily Price Direction (Accuracy)
Polymarket	146	Pre-split resol. ‡	Post-split resol. ‡	To resol.	Probability (Brier Score)
Memory	20	2024-02-01 – 2025-05-01	2025-05-01 – 2026-06-01	6 m / 9 m	Monthly Price (MAE, MSE)
SSD	10	2024-02-01 – 2025-05-01	2025-05-01 – 2026-08-01	6 m / 9 m	Monthly Price (MAE, MSE)

Table 1: Summary of the six evaluation domains. The table outlines the number of entities, chronological splits for the learning and evaluation windows, forecasting horizons, and target evaluation metrics for each task. Multiple horizons (e.g., 6 / 9 months) represent distinct experimental settings.

Method	Memory 6M		Memory 9M		SSD 6M		SSD 9M		Stock	Weather		Electricity
Method	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	Acc (%)	MAE	MSE	MAE	MSE
Autoformer	390.5	639.0	381.9	421.5	258.7	248.5	324.1	372.1	35.50	3.025	16.82	1751.6	6151.0
DLinear	317.8	474.7	400.8	570.4	211.8	221.8	340.6	479.8	29.67	3.020	16.69	1632.4	5354.9
TimesNet	376.7	628.7	435.1	556.4	210.7	200.4	336.6	446.2	33.42	3.016	16.78	1769.7	6000.1
PatchTST	350.1	558.5	427.2	988.2	215.9	222.2	434.2	714.9	31.17	3.044	17.10	1713.8	5582.4
Crossformer	300.9	386.2	318.1	369.0	211.2	188.0	323.2	400.9	31.33	2.948	15.96	1680.6	5496.1

Table 2: Performance comparison between SEER and baselines on time-series forecasting tasks. MSE values for Memory, SSD, and Electricity are scaled by

10^{-3}

. For the Stock domain, we convert the exact price predictions of baseline methods into price directions to evaluate accuracy; in each column, the best result is shaded and set in bold, and the second best is lightly shaded. Results including confidence intervals are detailed in Table 5 in Appendix C .

Backbone	SEER-Full vs. LLM-RAG				SEER-Full vs. SEER-NoK				SEER-NoK vs. LLM-RAG
	Win	Tie	Loss	$\Delta$	Win	Tie	Loss	$\Delta$	Win	Tie	Loss	$\Delta$
Claude Opus 4.8	71.8	20.3	7.9	+64.0	67.6	26.4	6.1	+61.5	32.2	44.1	23.6	+8.6
Claude Sonnet 4.6	54.8	30.4	14.8	+40.0	44.4	37.3	18.3	+26.2	40.5	41.1	18.4	+22.0
Gemini Flash 3.1	54.4	29.1	16.5	+37.9	42.1	38.5	19.5	+22.6	51.5	29.6	19.0	+32.5
Gemini Pro 3.1	41.3	30.5	28.3	+13.0	41.0	32.7	26.2	+14.8	33.3	33.0	33.8	-0.5
All backbones	55.6	27.6	16.9	+38.7	48.8	33.7	17.5	+31.3	38.7	36.6	24.7	+14.1

Table 3: Pairwise LLM-as-a-judge (Grok-4.20-Reasoning) evaluation on forecasting reasoning quality. Results demonstrate that SEER significantly outperforms both the baseline (LLM (Event RAG), denoted as LLM-RAG) and the knowledge-ablated variant (SEER w/o Knowledge, denoted as SEER-NoK). Win / Tie / Loss denote the percentage (%) of records where the first setting is better, tied, or loses.

\Delta

represents the net win rate (Win

-

Loss).

Backbone	Setting	Mem 6M	Mem 9M	SSD 6M	SSD 9M	Stock	Weather	Electricity	vs. LLM	vs. $+$ Events
		MAE	MAE	MAE	MAE	Acc. (%)	MAE	MAE	$\Delta$ (%)	$\Delta$ (%)
Claude Opus 4.8	LLM (No Events)	376.5	422.0	246.7	367.2	36.58	2.876	1642.2	–	–
	LLM (Event RAG)	287.9	372.8	234.5	296.6	36.92	2.590	1620.0	+10.2	–
	SEER w/o Knowledge	303.7	370.1	218.6	276.6	37.00	2.589	1659.6	+11.1	+0.9
	SEER	240.3	291.5	194.2	285.8	38.25	2.547	1473.3	+19.5	+10.5
Claude Sonnet 4.6	LLM (No Events)	387.5	441.2	259.0	403.7	35.58	3.003	1734.3	–	–

Table 4: Ablation study of SEER’s components across multiple LLM backbones. The best configuration per backbone is highlighted in bold . The final two columns, vs. LLM and vs.

+

Events , denote the average relative performance improvement across all seven tasks compared to the LLM (No Events) and LLM (Event RAG) baselines. Results including 95% confidence intervals are detailed in Table 6 (Appendix C ).

Figure 3: SEER transfers to non-time-series forecasting. Performance comparison of different agent configurations on the Polymarket event forecsating task (evaluated by Brier Score) and the Selective Stock Forecasting task (evaluated by Accuracy). The progressive improvements highlight that SEER’s evolving method, i.e., integrating both refined event retrieval and causal knowledge, consistently enhances predictive reasoning on event-driven tasks beyond standard numerical data.

Figure 4: Interpretable forecasting of SEER. (a) Memory price forecasting at the 2025-06-01 cut-off. In contrast to numerical models and the baseline LLM (Event RAG) that miss the regime shift, SEER leverages distilled causal knowledge (e.g., UPSIDE SQUEEZE) to accurately predict the price surge. (b) Selective stock forecasting. (Top) For the 29 mutually selected stocks, SEER and LLM (Event RAG) achieve similar accuracies (51.7% vs. 48.3%). However, SEER selectively targets 43 different tickers that it evaluates as more predictable, on which it achieves a higher prediction accuracy than the baseline (51.2% vs. 32.7%), leading to an improved overall performance. (Bottom) An example illustrating how SEER leverages learned causal knowledge to filter out uncertain stocks. The details for the cases are provided in Figures 15 to 18 in Appendix B .

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Method

Memory 6M

Memory 9M

SSD 6M

SSD 9M

Stock

Weather

Electricity

MAE

MSE

MAE

MSE

MAE

MSE

MAE

MSE

Acc (%)

MAE

MSE

MAE

MSE

Autoformer

[258.4, 548.5]

[244.3, 1279.3]

[258.3, 522.7]

[208.9, 675.9]

[89.9, 476.0]

[33.4, 579.8]

[124.8, 606.9]

[66.5, 936.9]

[31.67, 39.42]

[2.713, 3.357]

[13.47, 20.45]

[1392.1, 2150.3]

[3785.6, 8892.5]

DLinear

[198.0, 467.0]

[158.3, 990.3]

[250.0, 575.5]

[245.4, 976.4]

[70.8, 395.1]

[29.4, 521.7]

[122.1, 658.4]

[72.9, 1209.7]

[25.92, 33.33]

[2.700, 3.352]

[13.38, 20.16]

[1325.1, 1978.5]

[3379.1, 7717.5]

TimesNet

[245.6, 535.1]

[232.0, 1284.1]

[300.4, 587.4]

[270.6, 900.1]

[72.2, 392.0]

[26.9, 467.2]

[122.9, 649.6]

[67.7, 1128.0]

[30.17, 36.67]

[2.724, 3.324]

[13.56, 20.27]

[1490.4, 2077.2]

[4191.4, 8052.9]

PatchTST

[223.6, 504.0]

[197.1, 1151.6]

[226.6, 735.0]

[181.0, 2636.1]

[70.7, 407.9]

[30.4, 517.6]

[155.3, 855.6]

[102.6, 1824.8]

[28.00, 34.33]

[2.732, 3.380]

[13.69, 20.78]

[1430.9, 2025.5]

[3843.4, 7631.5]

Crossformer

[196.8, 434.9]

[143.2, 756.5]

[206.4, 458.3]

[146.9, 714.4]

[70.0, 399.1]

[23.6, 440.3]

[121.3, 611.8]

[70.7, 988.4]

[27.83, 34.83]

[2.646, 3.263]

[12.82, 19.29]

[1379.2, 2016.3]

[3584.9, 7771.8]

Appendix

Table 5: 95% confidence intervals for every cell of Table 2 . Each entry is the cluster-bootstrap interval (20,000 resamples; clusters are parts for Memory and SSD, cut-off days for Stock, forecast windows for Weather and Electricity) of the corresponding point estimate; all methods in a column are resampled with the same draws, so the intervals are paired. MSE for Memory, SSD and Electricity is scaled by

10^{-3}

. Bold and shading are carried over from Table 2 (best and second-best point estimate).

Backbone	Setting	Mem 6M	Mem 9M	SSD 6M	SSD 9M	Stock	Weather	Electricity	vs. LLM	vs. $+$ Events
		MAE	MAE	MAE	MAE	Acc. (%)	MAE	MAE	$\Delta$ (%)	$\Delta$ (%)
Claude Opus 4.8	LLM (No Events)	[240.2, 541.9]	[277.9, 589.9]	[81.1, 461.5]	[134.6, 693.7]	[33.08, 40.25]	[2.559, 3.212]	[1303.5, 2017.5]	–	–
	LLM (Event RAG)	[187.3, 412.4]	[247.2, 517.5]	[72.2, 456.2]	[114.3, 553.7]	[34.08, 39.67]	[2.290, 2.889]	[1308.9, 1964.1]	[+6.2, +14.8]	–
	SEER w/o Knowledge	[191.8, 444.9]	[245.6, 513.0]	[72.6, 411.9]	[102.7, 531.5]	[33.33, 40.75]	[2.315, 2.864]	[1371.8, 1976.6]	[+6.9, +15.9]	[-1.6, +3.7]
	SEER	[158.3, 335.7]	[191.8, 409.1]	[69.1, 349.6]	[111.5, 529.7]	[34.00, 42.33]	[2.280, 2.810]	[1217.1, 1768.8]	[+14.0, +26.1]	[+6.4, +15.9]
Claude Sonnet 4.6	LLM (No Events)	[254.6, 554.1]	[292.3, 615.4]	[86.5, 488.3]	[152.5, 757.7]	[32.58, 38.50]	[2.677, 3.367]	[1392.7, 2122.0]	–	–

Appendix

Table 6: 95% confidence intervals for Table 4 . Every cell shows the cluster-bootstrap interval (20,000 paired resamples) of the corresponding entry; the last two columns are the intervals of the average relative improvement over the seven tasks against LLM (No Events) and LLM (Event RAG), obtained by averaging the per-task paired bootstrap replicates. Bold follows the point estimates of Table 4 .

Panel of Figure 6 — Figure 6: SEER’s reasoning and forecast for the prompt in Figure 5 . It walks the Rule-1 regime checklist to an upside squeeze, citing the events by number, freezes the settled May print as the base (Rule 3, 5), places one $\times$ 1.780 step at the next quarter boundary (Rule 4, 5) and a $\times$ 1.43 second leg at the following one (Rule 6), then emits nine prices inside <prediction> tags.

Panel of Figure 16 — Figure 16: SEER’s reasoning and forecast for the prompt in Figure 15 (backbone: Claude-Opus-4.8). SEER applies Rule-1 criteria to predict an upward squeeze, citing specific events ([132], [156], [173], [66–68]). It sets the settled June price (875.18) as the baseline, treating the Q2 fluctuation as non-structural (Rules 3, 6). It then applies a $\times$ 1.780 multiplier for Q3 (July 2025; Rules 4, 5) and a $\times$ 1.43 multiplier for Q4 (October 2025; Rule 6), maintaining this plateau into Q1 2026.

Panel of Figure 18 — Figure 18: SEER’s response to Figure 17 (Claude-Opus-4.8). Using learned causal rules, SEER rejects risky candidates—e.g., an M&A-driven spike (MRK, Rule 19) and a headline reversal (CVX, Rules 3/41)—before selecting MU Down (Rule 59) and AMD Down (Rules 5/64). Both predictions correctly closed “Down” the next session, whereas all four baseline picks (MRK Up, AMD Up, CVX Down, XOM Down) failed.

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Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent research explored retrieving relevant historical time series segments to enhance forecasting. However, relying solely on time series similarity is often insufficient for retrieval under non-stationarity. To address this, we propose a multimodal approach: a Semantics-Enhanced Retrieval-Augmented Time Series Forecasting framework, SERAF. Unlike mainstream approaches that depend only on time series similarity, SERAF conducts dual retrieval over the time series and their self-generated textual descriptions. It retrieves two complementary sets of historical patterns and corresponding futures, which are selectively and jointly used to guide future predictions. Experiments across seven real-world datasets demonstrate the effectiveness of SERAF in bridging numerical and semantic views of time series compared with state-of-the-art baselines.

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