{ "version": "temporal-outlook-1", "asOf": "2026-10-05", "headline": "短期仍是分化，半年后看兑现，一年期保留更大的分歧。", "summary": "本轮最值得保留的判断是电子硬件与照护就业温和扩张，制造业整体偏平；IT 服务就业短期模型分歧，一年期主模型偏弱。行业入口仍应落到降本验收、维护和对账，但这些美国背景序列不能直接推出中国企业收入或某只资产的涨跌。", "scope": "实际运行 11 种方法：5 个新增统计或机器学习模型、2 套原有神经网络、3 个基线及 1 个中位数组合。数值仍基于同一份 2026-10-05 官方数据快照，没有把旧帖数量当作训练样本。", "selectionNote": "主线模型按 2017—2022 年末六个起点的历史验证表现逐系列、逐期限选定，随后在 2024 年起的结果上检查。此选型规则是在今天拟定的，并非当年实盘执行。神经网络不参与可能含预训练重叠的早期选型。", "calendarNote": "期限从各序列最后可用观察月起算。工业数据最后为 2026 年 8 月，因此 +1 月是 9 月补估；就业最后为 9 月，+1 月才是 10 月。页面显示具体目标年月，不能把所有第一步都叫今天之后的未来。", "timeline": [ { "period": "1—3 个月｜至 2026 年秋冬", "title": "验证现有订单与成本，不急于扩产", "text": "电子产品主线小幅增加；制造业接近持平。IT 就业的传统模型与神经网络方向不一，因此将此前“短期偏弱”修订为“分歧、接近持平到小幅走弱”。优先做可验收、可回款的小项目。", "gate": "到 2026 年底核验付费试点、交付成本与客户验收；只有意向单时不增加固定资产。" }, { "period": "6 个月｜至 2027 年 2—3 月", "title": "电子与照护偏增，IT 方向仍需后续数据", "text": "主线预计电子产出约增 0.63%、照护相关就业约增 0.95%；制造业持平。IT 主线约增 0.23%，两套神经网络约降 0.74%—0.79%，属于需要跟踪的分歧。", "gate": "2027Q1 将试点节省与真实续费核对；若没有付费复购，暂停推广，不用宏观趋势替代客户证据。" }, { "period": "12 个月｜至 2027 年 8—9 月", "title": "温和增长主线成立与否，要看需求能否持续", "text": "主线预计电子产出约增 1.26%、照护相关就业约增 1.95%，IT 服务就业约降 1.26%；制造业仍接近持平。电子产品各方法给出的一年期末变化约为 −0.10% 至 +9.88%，分歧已足以影响扩产判断。", "gate": "用实际运营设备、续费和净回款检验 2027 年业务结果；神经网络 12 个月预测目前没有合格的完整历史检查窗口。" } ], "interpretations": { "IPG334S": "多数趋势与神经网络方法倾向电子硬件继续增长，但增幅分歧明显。研究已有订单的交付瓶颈，尚不足以支持按乐观曲线提前扩产。", "IPMAN": "主线从短期到一年基本持平；机器人等细分市场增长可以与制造业总量平稳共存。必须回到客户所在工序与地区判断。", "IPG2211A2N": "秋季回落、之后回升主要有季节形状。12 个月点预测也没有形成强劲上行共识；总公用事业产出不能衡量数据中心的局部电网瓶颈。", "CES6054150001": "新增传统模型使短期结论出现分歧；一年期主线与两神经网络都偏弱。可将成本压力作为客户访谈假说，不能据此断言 AI 导致裁员或软件收入必跌。", "CES6562000001": "照护相关就业的中长期主线偏增。它支持持续观察服务需求，却仍不能代替中国具体地区的支付方、服务密度和利润验证。" }, "limits": [ "本轮是修订后历史数据的回顾性滚动检查，没有还原历史版本、当时发布滞后或当时可获得的信息。", "每个传统模型按预测起点重新拟合，最多使用此前 240 个月；机器学习标签和标准化只用起点之前数据，参数不按检查段误差调整。", "选择段的所有结果最晚至 2023 年 12 月，检查段结果从 2024 年 1 月开始；长周期起点会重叠，数量不等于独立样本数。", "神经网络只在 2025 年 11 月之后的合格起点比较；1、3、6、12 个月分别只有 3、2、1、0 个近期起点。不能把近期误差与不同窗口的长期误差直接排行。", "本轮扩展选择策略在四个期限的平均误差均低于选定基线；这是本数据集结果，不是已校准的未来命中率，也不能证明长期普遍优胜。", "模型间最大最小值表示方法分歧，不是概率区间。神经网络分位带未经当前任务覆盖率校准。未进行模拟投资或回报检验。", "Web3、低空、机器人和中国养老没有在本轮获得足够的同口径长序列，继续保留行业证据与条件推演；不拿美国宏观代理序列替代它们。", "24—36 个月继续采用已有条件情景，不把一年数值简单复制到更远未来。无自动监控或定时运行。" ], "methods": [ { "id": "last", "name": "最后值", "family": "基线", "description": "保持最后实测值不变。" }, { "id": "seasonal", "name": "去年同月", "family": "基线", "description": "重复上一年同月，保留季节形状。" }, { "id": "trend12", "name": "近期线性趋势", "family": "基线", "description": "延长最近 12 个月的线性趋势。" }, { "id": "ets", "name": "阻尼指数平滑", "family": "统计模型", "description": "近期权重更高，限制趋势无限延长；未季调序列加入季节项。" }, { "id": "theta", "name": "Theta", "family": "统计模型", "description": "结合长期趋势与短期水平；未季调序列先处理季节性。" }, { "id": "arima", "name": "ARIMA / SARIMA", "family": "统计模型", "description": "拟合差分后的相关性；仅未季调系列使用季节项。" }, { "id": "ridge", "name": "岭回归", "family": "机器学习", "description": "过去 24 月相对变化与月份特征，直接预测 12 个步长。" }, { "id": "trees", "name": "ExtraTrees 随机树", "family": "机器学习", "description": "用同一组滞后特征学习非线性关系；不等于神经网络。" }, { "id": "median", "name": "八模型中位数组合", "family": "集成", "description": "逐月取前八种方法预测中位数，不依据检查段调权。" }, { "id": "Chronos-2", "name": "Chronos-2", "family": "预训练神经网络", "description": "固定权重零样本推理，仅在权重发布后的观察起点比较误差。" }, { "id": "Chronos-Bolt", "name": "Chronos-Bolt", "family": "预训练神经网络", "description": "固定权重零样本推理，长期输出仍需要之后的真实数据检验。" } ], "sources": [ { "title": "指数平滑实现与参数", "url": "https://www.statsmodels.org/stable/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html" }, { "title": "Theta 模型实现", "url": "https://www.statsmodels.org/stable/generated/statsmodels.tsa.forecasting.theta.ThetaModel.html" }, { "title": "随机树实现与参数", "url": "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html" }, { "title": "Chronos 官方实现", "url": "https://github.com/amazon-science/chronos-forecasting" } ], "run": { "protocol": { "version": "temporal-1", "asOf": "2026-10-05", "contextMonths": 240, "horizons": [ 1, 3, 6, 12 ], "selectionOrigins": [ "2017-12-01", "2018-12-01", "2019-12-01", "2020-12-01", "2021-12-01", "2022-12-01" ], "auditOrigins": [ "2023-12-01", "2024-03-01", "2024-06-01", "2024-09-01", "2024-12-01", "2025-03-01", "2025-06-01", "2025-09-01", "2025-12-01", "2026-03-01", "2026-06-01" ], "auditObservationCutoff": "2026-08-01", "neuralEarliestOrigin": "2025-11-01", "localCandidates": [ "last", "seasonal", "trend12", "ets", "theta", "arima", "ridge", "trees", "median" ], "allMethods": [ "last", "seasonal", "trend12", "ets", "theta", "arima", "ridge", "trees", "median", "Chronos-2", "Chronos-Bolt" ], "selection": "Per series and horizon, choose lowest mean path MAPE across six selection origins. All selection outcomes end by 2023-12; audit outcomes start 2024-01. Never choose on audit.", "baselineSelection": "Select among last/seasonal/trend12 using same selection data.", "median": "Pointwise median of the eight local methods; require all eight to succeed.", "refit": "Each origin independently uses its latest 240 observed months; no future target or scaler fitted on future data.", "neural": "Fixed pretrained weights. Compared only on audit origins >= 2025-11 and complete target paths; not eligible for historical selector. 12-month neural audit is unavailable.", "metric": "Mean absolute percentage error over steps 1..h at each origin, then equal-weight origins and series. Origins overlap; sample count is not independent observations.", "direction": "Endpoint versus last observation; not year-over-year. For nonseasonal interpretation compare the same forecast month a year earlier.", "vintage": "Frozen current FRED vintage; not historical release-time data, and publication lag is not reconstructed.", "params": { "ETS": "additive damped trend; additive 12-month seasonality only for unadjusted series; use_brute=False", "Theta": "theta=2; deseasonalize only unadjusted series, period 12", "ARIMA": "(1,1,1); additional (0,1,1,12) only for unadjusted series; log input; L-BFGS maxiter 300; retry Powell maxiter 1000 on nonconvergence; still nonconverged fits excluded", "Ridge": "alpha=10, 24 relative lag values and calendar sin/cos, direct 12 outputs, train-only standardization", "ExtraTrees": "100 trees, min_samples_leaf=4, max_depth=8, seed=42; same direct relative-target training set", "baseline": "last value, same month last year, ordinary least squares over last 12 months" } }, "runtime": { "torch": "2.14.1+cpu", "chronos": "2.3.2", "statsmodels": "0.15.0", "sklearn": "1.8.0", "numpy": "2.3.5", "pandas": "2.2.3", "seed": 42, "device": "CPU" }, "modelProvenance": { "Chronos-2": { "id": "amazon/chronos-2", "sha": "29ec3766d36d6f73f0696f85560a422f50e8498c", "lastModified": "2026-06-05T11:55:42.000Z", "createdAt": "2025-10-30T14:54:39.000Z", "weightsSha256": "ddcda3c7508bf2528087723e98a20707cc04b7f370ae275a9fd88078ddba4f42" }, "Chronos-Bolt": { "id": "amazon/chronos-bolt-base", "sha": "5d9f166d69f47aef3401367a7b842e78fe97b121", "lastModified": "2025-11-21T12:53:22.000Z", "createdAt": "2024-11-25T09:47:43.000Z", "weightsSha256": "31f875483a3215bc6880a0837ea608a13ce55f88ad90538c3cdd0b29aeb60b36" } }, "summary": { "1": { "selectedMAPE": 1.045833479620563, "baselineMAPE": 1.1487799061595223, "originsPerSeries": 11, "localMAPE": { "last": 2.3835939034041136, "seasonal": 2.783457402089785, "trend12": 4.253825352384914, "ets": 1.0215625684293015, "theta": 0.922418130786537, "arima": 1.050639198841537, "ridge": 0.9088687494120549, "trees": 0.9077098231747062, "median": 0.9503918408098496 }, "recentOriginsPerSeries": 3, "recentMAPE": { "last": 2.24518046080373, "seasonal": 2.9455963561785334, "trend12": 4.474777410822893, "ets": 0.9611824684252095, "theta": 0.7816589427926955, "arima": 0.8536858869845686, "ridge": 0.6747398484371472, "trees": 0.7829099720395918, "median": 0.8035283864144536, "Chronos-2": 0.6099436064913704, "Chronos-Bolt": 0.7752914671359646 } }, "3": { "selectedMAPE": 1.0157389209506824, "baselineMAPE": 1.161884142709622, "originsPerSeries": 10, "localMAPE": { "last": 2.2105756992624714, "seasonal": 2.7544107041394663, "trend12": 3.0480053498893733, "ets": 1.03919760356161, "theta": 1.052358373702939, "arima": 1.1314982401883351, "ridge": 1.0025221701477782, "trees": 0.9519834504189145, "median": 1.0281959595717116 }, "recentOriginsPerSeries": 2, "recentMAPE": { "last": 2.442084034504851, "seasonal": 2.9032233242631893, "trend12": 3.3255733877245524, "ets": 1.0187199981576878, "theta": 1.0780121914751892, "arima": 1.1245733549472499, 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138.6877, 138.6877, 138.6877, 138.6877, 138.6877 ] }, "seasonal": { "point": [ 127.4438, 129.414, 130.2655, 130.0517, 131.9862, 132.3677, 132.4777, 135.2003, 137.0149, 137.5457, 139.3896, 138.6877 ] }, "trend12": { "point": [ 140.50963484848484, 141.59002995337994, 142.67042505827504, 143.75082016317015, 144.83121526806525, 145.91161037296035, 146.99200547785546, 148.07240058275053, 149.15279568764564, 150.23319079254074, 151.31358589743584, 152.39398100233095 ] }, "ets": { "point": [ 139.18406213827421, 139.63508899203524, 140.0449212821681, 140.41732151651107, 140.75570855403245, 141.06318899202017, 141.3425856865202, 141.59646366785913, 141.82715368917118, 142.03677362411904, 142.2272479102526, 142.4003252165074 ] }, "theta": { "point": [ 138.83313179848923, 138.97846003591525, 139.12378827334123, 139.26911651076725, 139.41444474819323, 139.55977298561922, 139.70510122304523, 139.85042946047122, 139.99575769789723, 140.14108593532322, 140.2864141727492, 140.43174241017522 ] }, 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