{"id":173,"date":"2026-10-13T11:23:17","date_gmt":"2026-10-13T11:23:17","guid":{"rendered":"https:\/\/therisk.global\/food-nexus\/?page_id=173"},"modified":"2026-10-13T17:59:22","modified_gmt":"2026-10-13T17:59:22","slug":"forecasting","status":"publish","type":"page","link":"https:\/\/therisk.global\/food-nexus\/stem\/forecasting\/","title":{"rendered":"Forecasting for food security"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/food-nexus\/stem\/forecasting\/ \u00b7 source template \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-nqxscocf.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"console\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G02 \u00b7 METHOD \u00b7 forecasting<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Food Nexus<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Method page \u00b7 Forecasting and time-series analysis<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Forecasting for food security<\/h1><p class=\"nx-tagline\">Forecasting gives food systems lead time: seasonal production outlooks, projections of lean-season food insecurity, staple price outlooks and pest alerts. Its value lies in the lead time it gives and the skill it can demonstrate on past seasons, not in point predictions.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/food-nexus\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/food-nexus\/stem\/\">Back to STEM foundation of Food Nexus<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Method record<\/strong><\/span><span class=\"nx-eyebrow\">forecasting<\/span><\/div><ol class=\"nx-register-rows\"><li><div><span class=\"nx-register-k\">Method<\/span><span class=\"nx-register-v\">Forecasting and time-series analysis<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Steward<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/climate-hazards\/\">Climate and Hazards<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\">Food Nexus<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Data<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-data\">4 inputs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Checks<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-checks\">4 validation steps<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Canonical page<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/forecasting\/\">STEM Commons<\/a><\/span><\/div><\/li><\/ol><p class=\"nx-register-foot\">GCRI \u00b7 Established \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-application\"><span>02<\/span>What forecasting and time-series\u2026<\/a><a href=\"#nx-sec-terms\"><span>03<\/span>Terms used on this page<\/a><a href=\"#nx-sec-field\"><span>04<\/span>Food systems and trade routes in the\u2026<\/a><a href=\"#nx-sec-canvas\"><span>05<\/span>The method as a run graph<\/a><a href=\"#nx-sec-data\"><span>06<\/span>What the method consumes<\/a><a href=\"#nx-sec-checks\"><span>07<\/span>How results are checked<\/a><a href=\"#nx-sec-connections\"><span>08<\/span>Topics that rely on this method<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"food\" data-variant=\"split\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Food security forecasting is the prediction of production, prices and food insecurity ahead of a season or lean period from climate forecasts, crop condition and market data. Its value lies in lead time and skill shown on past seasons. Forecasts set the triggers for anticipatory action, and agencies such as FEWS NET publish outlooks.<\/p><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"supply\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">What forecasting and time-series analysis does here.<\/h2><p class=\"nx-lede\">Forecasting and time-series analysis predict future values of variables from past observations and explanatory information. They support early warning and planning across hazards, markets and health. In food and agriculture, trade, resources and supply chains and climate and hazards, forecasts trigger early action and anticipatory finance before shocks materialise. Probabilistic forecasts allow decisions to account for uncertainty across connected systems. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/forecasting\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M5 22h14\"\/><path d=\"M5 2h14\"\/><path d=\"M17 22v-4.172a2 2 0 0 0-.586-1.414L12 12l-4.414 4.414A2 2 0 0 0 7 17.828V22\"\/><path d=\"M7 2v4.172a2 2 0 0 0 .586 1.414L12 12l4.414-4.414A2 2 0 0 0 17 6.172V2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Seasonal production outlooks<\/h3><p class=\"nx-p nx-mt-1\">Seasonal climate forecasts and current crop condition are combined in statistical or crop models to give the probability of below-normal production by region, updated as the season progresses.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Staple price forecasting<\/h3><p class=\"nx-p nx-mt-1\">Time-series and structural models project staple prices in monitored markets from world prices, exchange rates, harvests and seasonality, with forecast intervals that widen with lead time.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Anticipatory action triggers<\/h3><p class=\"nx-p nx-mt-1\">Thresholds on forecast indicators are set and tested on past seasons, so agencies know the expected hits and false alarms before committing finance.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Seasonal climate forecast<\/td><td data-label=\"Definition\">A probabilistic forecast of rainfall and temperature for the coming months, issued by centres such as Copernicus C3S.<\/td><\/tr><tr><td data-label=\"Term\">Trigger<\/td><td data-label=\"Definition\">A pre-agreed forecast or observed threshold that releases planned action or finance when it is crossed.<\/td><\/tr><tr><td data-label=\"Term\">Hindcast<\/td><td data-label=\"Definition\">A forecast run for past seasons to measure how skilful the forecasting system would have been.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"food\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Food systems and trade routes in the Sahel.<\/h2><p class=\"nx-lede\">Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one, shown for the Sahel as an example geography for this subject.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-map nx-cmap\" data-nx-map=\"ctx\" data-cmap=\"sahel:food\" data-ramp=\"orange\" data-basis=\"case\" data-focus=\"0 0 2000 1000|332 96 1031 750|1424 158 1862 834|858 164 1514 692|156 128 626 534\"><figure class=\"nx-fk\" data-fk=\"map-sahel-food\" data-ramp=\"orange\" 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\"><figcaption class=\"nx-fk-cap\"><span>Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one.<\/span><span data-honesty=\"true\">Indicator derived from Natural Earth at map scale; a screening view, not an assessment.<\/span><\/figcaption><\/figure><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G02\/stem\/forecasting\/#map\" data-nxf-title=\"Food Nexus live map\" data-nxf-height=\"560\" hidden><\/div><div class=\"nx-map-hud\"><span class=\"nx-eyebrow\">GCRI \u00b7 Food Nexus \u00b7 field<\/span><span class=\"nx-eyebrow nx-map-hud-right\">DESIGN FIGURE<\/span><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-canvas\" class=\"nx-section\" data-hue=\"supply\" data-tone=\"alt\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">The method as a run graph.<\/h2><p class=\"nx-lede\">Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Method canvas<\/strong> \u00b7 Forecasting and time-series analysis as applied here<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g02-stem-forecasting-flood-ews-t nx-dag-g02-stem-forecasting-flood-ews-d\" data-nx-z=\"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\"><title id=\"nx-dag-g02-stem-forecasting-flood-ews-t\">Forecasting and time-series analysis in Food Nexus: flood forecasting and warning chain<\/title><desc id=\"nx-dag-g02-stem-forecasting-flood-ews-d\">Rainfall forecasts drive a hydrological then a hydraulic model; inundation maps are compared with thresholds; the mandated authority issues the warning, it reaches people through every channel, and action follows. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 01<\/strong><span>Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by forecasting and time-series analysis on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Seasonal climate forecasts (Copernicus C3S, NMME)<\/td><td data-label=\"Role\">Forcing for outlooks<\/td><td data-label=\"Label\"><span class=\"nx-chip\">FORECAST<\/span><\/td><\/tr><tr><td data-label=\"Data\">Market price time series<\/td><td data-label=\"Role\">Price model training<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><tr><td data-label=\"Data\">Crop condition indicators<\/td><td data-label=\"Role\">In-season predictors<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Past IPC and FEWS NET classifications<\/td><td data-label=\"Role\">Verification of food insecurity projections<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G02\/stem\/forecasting\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G02\/stem\/forecasting\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"food\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Hindcast verification<\/td><td data-label=\"How it is done\">Skill is measured on past forecasts by lead time, region and season<\/td><\/tr><tr><td data-label=\"Check\">Reliability and sharpness<\/td><td data-label=\"How it is done\">Probabilities are checked for calibration as well as for resolution<\/td><\/tr><tr><td data-label=\"Check\">Rolling-origin evaluation<\/td><td data-label=\"How it is done\">Models are re-trained and tested through time to expose structural breaks<\/td><\/tr><tr><td data-label=\"Check\">Trigger performance record<\/td><td data-label=\"How it is done\">Hits, misses and false alarms are published for every trigger<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"supply\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/agricultural-production\/\"><span>Agricultural production<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/food-security-and-access\/\"><span>Food security and access<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/storage-and-cold-chain\/\"><span>Storage and cold chain<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/food-trade-and-markets\/\"><span>Food trade and markets<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/food-system-shocks\/\"><span>Food system shocks<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/climate-hazards\/\">Climate and Hazards<\/a> stewards forecasting and time-series analysis for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/forecasting\/\">Canonical page: Forecasting and time-series analysis<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section\" data-hue=\"food\" data-tone=\"alt\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/stem\/mathematics\/forecasting\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Canonical method page<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Forecasting and time-series analysis<\/span><span class=\"nx-dest-why\">Platform application links to the canonical method page.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/climate-hazards\/stem\/forecasting\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Forecasting for hazards<\/span><span class=\"nx-dest-why\">Climate and Hazards stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-12-sec\" class=\"nx-section\" data-hue=\"supply\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of forecasting and time-series analysis in this field?<\/summary><div class=\"nx-answer\">Structural breaks and regime changes undermine models trained on the past. 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