{"id":3502,"date":"2023-08-31T11:12:58","date_gmt":"2023-08-31T11:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/inflation-prediction\/"},"modified":"2026-10-06T13:26:57","modified_gmt":"2026-10-06T11:26:57","slug":"inflation-prediction","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/inflation-prediction\/","title":{"rendered":"Forecasting monthly inflation in Poland"},"content":{"rendered":"<style>\n.ndb{--ndb-code-bg:#f3f7fa;--ndb-code-fg:#12354b;box-sizing:border-box;width:100%;margin-left:0;margin-right:0;padding:22px 0 14px;background:#fff;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.ndb *{box-sizing:border-box}.ndb-wrap{width:min(calc(100% - 48px),1160px);margin:0 auto}.ndb a{text-decoration:none}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;}\n.ndb-executive h2{margin:0 0 12px;font-size:30px}.ndb-executive 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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Forecast Poland\u2019s annual inflation rate<\/h2>\n<p>Use 12 monthly observations to forecast the next three observations of Poland\u2019s all-items HICP annual rate of change. The source contains 348 monthly values from January 1997 through December 2025.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>348<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>12<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>57<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>3<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-inflationprediction-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Business decision<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>Economic analysts can compare short-horizon forecasts of an explicitly defined price-index rate. The target is the change relative to the same month one year earlier, expressed in percent, not a month-on-month inflation rate.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Defined rate<\/h3><p>Forecast year-on-year HICP inflation.<\/p><\/div><div class=\"ndb-value-card\"><h3>Three horizons<\/h3><p>Inspect one-, two- and three-month-ahead outputs.<\/p><\/div><div class=\"ndb-value-card\"><h3>Chronological evidence<\/h3><p>Retain the ordered series and saved subset roles.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Economic research<\/span><span>Forecast evaluation<\/span><span>Time-series analysis<\/span><\/div><div class=\"ndb-note\">Historical forecasting benchmark for one country and one price-index definition.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The Eurostat series is prc_hicp_manr, filtered to PL, CP00, RCH_A and monthly frequency. The derived file retains the chronological numeric series; Neural Designer constructs the lagged inputs and three-step targets.<\/p><p>Source: <a href=\"https:\/\/ec.europa.eu\/eurostat\/databrowser\/view\/prc_hicp_manr\/default\/table\">HICP &#8211; monthly data (annual rate of change): Poland, all-items<\/a>. Dataset license: EC-Reuse-2011-833-EU. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>monthly observation<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>348<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>1<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>12<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>3<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>221<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>55<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>57<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>15<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">inflation<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-inflationprediction-t0-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-inflationprediction-t0-s1-20261006\" role=\"img\" aria-label=\"inflation time series\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t0-s1-20261006.png\" alt=\"inflation time series\"><script 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The source series is ordered in time. The saved configuration uses 12 lags and 3 steps ahead. Role counts describe source rows; usable window counts can differ after lagging and subset-boundary exclusion.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 12 encoded inputs and 3 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>12 \u00d7 1<\/td><td>12 \u00d7 1<\/td><td><\/td><\/tr><tr><th scope=\"row\">Recurrent<\/th><td>12 \u00d7 1<\/td><td>5<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>5<\/td><td>3<\/td><td>Identity<\/td><\/tr><tr><th scope=\"row\">Unscaling<\/th><td>3<\/td><td>3<\/td><td><\/td><\/tr><tr><th scope=\"row\">Clamping<\/th><td>3<\/td><td>3<\/td><td><\/td><\/tr><\/tbody><\/table><\/div><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t2-s1-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t2-s1-20261006.png\" alt=\"Forecasting monthly inflation in Poland \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses QuasiNewton with NormalizedSquaredError.<\/p><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>136<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:00<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum validation error increases<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.054<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.02<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-inflationprediction-t3-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-inflationprediction-t3-s2-20261006\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t3-s2-20261006.png\" alt=\"Quasi-Newton method error history\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135],\"y\":[1.090000033378601,0.9390000104904175,0.722000002861023,0.32199999690055847,0.15199999511241913,0.11999999731779099,0.10100000351667404,0.07540000230073929,0.0632999986410141,0.05779999867081642,0.05620000138878822,0.0560000017285347,0.0560000017285347,0.055799998342990875,0.05570000037550926,0.05559999868273735,0.05559999868273735,0.05550000071525574,0.055399999022483826,0.05530000105500221,0.0551999993622303,0.05510000139474869,0.05490000173449516,0.05490000173449516,0.05490000173449516,0.05480000004172325,0.05480000004172325,0.05469999834895134,0.05460000038146973,0.054499998688697815,0.0544000007212162,0.0544000007212162,0.05429999902844429,0.05429999902844429,0.05420000106096268,0.05420000106096268,0.05420000106096268,0.05420000106096268,0.05420000106096268,0.054099999368190765,0.05400000140070915,0.05400000140070915,0.05389999970793724,0.05389999970793724,0.05380000174045563,0.053700000047683716,0.053700000047683716,0.053599998354911804,0.053599998354911804,0.05350000038743019,0.05350000038743019,0.05339999869465828,0.053300000727176666,0.053300000727176666,0.053199999034404755,0.05310000106692314,0.05299999937415123,0.052799999713897705,0.052299998700618744,0.052299998700618744,0.052299998700618744,0.05209999904036522,0.051600001752376556,0.05130000039935112,0.05119999870657921,0.05090000107884407,0.05040000006556511,0.05009999871253967,0.049800001084804535,0.0494999997317791,0.04910000041127205,0.048900000751018524,0.048700001090765,0.04859999939799309,0.04830000177025795,0.04809999838471413,0.04769999906420708,0.04749999940395355,0.047200001776218414,0.047200001776218414,0.04699999839067459,0.04690000042319298,0.046799998730421066,0.04659999907016754,0.04650000110268593,0.04639999940991402,0.04619999974966049,0.04610000178217888,0.045899998396635056,0.04580000042915344,0.04569999873638153,0.04560000076889992,0.04529999941587448,0.04529999941587448,0.04520000144839287,0.045099999755620956,0.04490000009536743,0.04479999840259552,0.044599998742341995,0.04439999908208847,0.04439999908208847,0.04430000111460686,0.04430000111460686,0.044199999421834946,0.04410000145435333,0.04410000145435333,0.04399999976158142,0.04390000179409981,0.043699998408555984,0.04360000044107437,0.04340000078082085,0.04320000112056732,0.04309999942779541,0.0430000014603138,0.0430000014603138,0.042899999767541885,0.042899999767541885,0.04280000180006027,0.04270000010728836,0.04259999841451645,0.042500000447034836,0.04230000078678131,0.0421999990940094,0.042100001126527786,0.041999999433755875,0.041999999433755875,0.04190000146627426,0.04190000146627426,0.04179999977350235,0.04179999977350235,0.04179999977350235,0.04170000180602074,0.04170000180602074,0.04170000180602074,0.041600000113248825,0.04149999842047691]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135],\"y\":[0.9020000100135803,0.3610000014305115,0.048900000751018524,0.08829999715089798,0.028200000524520874,0.027000000700354576,0.023600000888109207,0.022700000554323196,0.02160000056028366,0.020500000566244125,0.020400000736117363,0.020400000736117363,0.020400000736117363,0.020600000396370888,0.020899999886751175,0.0210999995470047,0.021199999377131462,0.021199999377131462,0.020899999886751175,0.020400000736117363,0.020099999383091927,0.019899999722838402,0.019899999722838402,0.019899999722838402,0.019899999722838402,0.01979999989271164,0.01979999989271164,0.019899999722838402,0.019899999722838402,0.01979999989271164,0.01979999989271164,0.019700000062584877,0.019600000232458115,0.019600000232458115,0.019600000232458115,0.019500000402331352,0.019600000232458115,0.019600000232458115,0.01979999989271164,0.020099999383091927,0.02019999921321869,0.0203000009059906,0.020800000056624413,0.0203000009059906,0.0203000009059906,0.020400000736117363,0.02019999921321869,0.02019999921321869,0.020400000736117363,0.020600000396370888,0.021299999207258224,0.020899999886751175,0.021199999377131462,0.02160000056028366,0.021700000390410423,0.023099999874830246,0.0210999995470047,0.02160000056028366,0.02280000038444996,0.023499999195337296,0.021199999377131462,0.02160000056028366,0.02250000089406967,0.02250000089406967,0.021199999377131462,0.0210999995470047,0.021800000220537186,0.02199999988079071,0.02019999921321869,0.02019999921321869,0.020500000566244125,0.020899999886751175,0.022099999710917473,0.020600000396370888,0.01979999989271164,0.019899999722838402,0.020500000566244125,0.02019999921321869,0.019700000062584877,0.019600000232458115,0.019700000062584877,0.019700000062584877,0.019600000232458115,0.019600000232458115,0.019700000062584877,0.02019999921321869,0.020899999886751175,0.0210999995470047,0.020800000056624413,0.021700000390410423,0.02199999988079071,0.023099999874830246,0.023800000548362732,0.023499999195337296,0.0210999995470047,0.021800000220537186,0.022099999710917473,0.021800000220537186,0.021299999207258224,0.0215000007301569,0.021199999377131462,0.0210999995470047,0.0210999995470047,0.0210999995470047,0.0210999995470047,0.020800000056624413,0.020800000056624413,0.0203000009059906,0.0203000009059906,0.020400000736117363,0.020500000566244125,0.020600000396370888,0.020500000566244125,0.019999999552965164,0.019999999552965164,0.020099999383091927,0.020099999383091927,0.020099999383091927,0.0203000009059906,0.020500000566244125,0.021299999207258224,0.0210999995470047,0.0210999995470047,0.02160000056028366,0.020600000396370888,0.020800000056624413,0.020999999716877937,0.021199999377131462,0.021199999377131462,0.0210999995470047,0.020999999716877937,0.020899999886751175,0.020899999886751175,0.020999999716877937,0.021199999377131462,0.021199999377131462]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Training history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,135],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.2002000000000002],\"title\":{\"text\":\"Normalized squared error\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Quasi-Newton method error history. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><p>A persistence forecast provides a transparent baseline for future evaluation. No independently verified persistence score is bundled with this saved run, so no numerical improvement over that baseline is claimed.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 57 source records.<\/p><h3>inflation (t+1) goodness-of-fit parameters<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Determination<\/th><td>0.969<\/td><\/tr><\/tbody><\/table><\/div><h3>inflation (t+2) goodness-of-fit parameters<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Determination<\/th><td>0.909<\/td><\/tr><\/tbody><\/table><\/div><h3>inflation (t+3) goodness-of-fit parameters<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Determination<\/th><td>0.837<\/td><\/tr><\/tbody><\/table><\/div><h3>Average goodness-of-fit parameter<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Average determination<\/th><td>0.905<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-inflationprediction-t4-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-inflationprediction-t4-s2-20261006\" role=\"img\" aria-label=\"inflation (t+1) goodness-of-fit chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t4-s2-20261006.png\" alt=\"inflation (t+1) goodness-of-fit chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"marker\":{\"color\":\"#209fdf\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Predicted inflation (t+1)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[3.5999999046325684,4.400000095367432,5.099999904632568,4.599999904632568,4.099999904632568,4.699999809265137,5,5.599999904632568,6.400000095367432,7.400000095367432,8,8.699999809265137,8.100000381469727,10.199999809265137,11.399999618530273,12.800000190734863,14.199999809265137,14.199999809265137,14.800000190734863,15.699999809265137,16.399999618530273,16.100000381469727,15.300000190734863,15.899999618530273,17.200000762939453,15.199999809265137,14,12.5,11,10.300000190734863,9.5,7.699999809265137,6.300000190734863,6.300000190734863,6.199999809265137,4.5,3.700000047683716,2.700000047683716,3,2.799999952316284,2.9000000953674316,4,4,4.199999809265137,4.199999809265137,3.9000000953674316,3.9000000953674316,4.300000190734863,4.300000190734863,4.400000095367432,3.700000047683716,3.5,3.4000000953674316,2.9000000953674316,2.700000047683716,2.9000000953674316,2.9000000953674316],\"y\":[4.312099933624268,4.17540979385376,5.559749603271484,6.295763969421387,4.879961013793945,4.399768829345703,5.938719749450684,5.9361891746521,6.698807716369629,7.851289749145508,9.0205078125,9.406482696533203,10.272193908691406,8.895017623901367,13.124300003051758,13.74097728729248,15.271734237670898,17.407928466796875,16.540552139282227,17.704906463623047,19.303218841552734,20.175125122070312,19.26956558227539,17.814430236816406,19.414878845214844,21.640226364135742,17.232688903808594,15.658679008483887,13.702863693237305,11.821378707885742,11.42795181274414,10.49537181854248,7.843055725097656,6.451154708862305,7.460508346557617,7.125339508056641,4.118228912353516,3.8758654594421387,2.7347402572631836,3.709517478942871,3.1732916831970215,3.3671822547912598,5.380841255187988,4.560233116149902,4.8370184898376465,4.906193733215332,4.2882795333862305,4.515824794769287,5.254457950592041,4.921232223510742,5.089521884918213,3.872342586517334,3.9405789375305176,3.9522461891174316,3.0493078231811523,3.0389928817749023,3.546363592147827]},{\"connectgaps\":false,\"line\":{\"color\":\"#3c3c3c\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Predicted inflation (t+1)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[-5,20.005],\"y\":[-5,20.005]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Measured and modelled values\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-5.5001,20.5051],\"title\":{\"text\":\"Actual inflation (t+1)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-5.5001,20.5051],\"title\":{\"text\":\"Predicted inflation (t+1)\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>inflation (t+1) goodness-of-fit chart. 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-inflationprediction-t5-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-inflationprediction-t5-s1-20261006\" role=\"img\" aria-label=\"inflation (t+1) output plot\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t5-s1-20261006.png\" alt=\"inflation (t+1) output plot\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Targets\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],\"y\":[3.5999999046325684,4.400000095367432,5.099999904632568,4.599999904632568,4.099999904632568,4.699999809265137,5,5.599999904632568,6.400000095367432,7.400000095367432,8,8.699999809265137,8.100000381469727,10.199999809265137,11.399999618530273,12.800000190734863,14.199999809265137,14.199999809265137,14.800000190734863,15.699999809265137,16.399999618530273,16.100000381469727,15.300000190734863,15.899999618530273,17.200000762939453,15.199999809265137,14,12.5,11,10.300000190734863,9.5,7.699999809265137,6.300000190734863,6.300000190734863,6.199999809265137,4.5,3.700000047683716,2.700000047683716,3,2.799999952316284,2.9000000953674316,4,4,4.199999809265137,4.199999809265137,3.9000000953674316,3.9000000953674316,4.300000190734863,4.300000190734863,4.400000095367432,3.700000047683716,3.5,3.4000000953674316,2.9000000953674316,2.700000047683716,2.9000000953674316,2.9000000953674316]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Outputs\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],\"y\":[3.680000066757202,3.562999963760376,4.749000072479248,5.382999897003174,4.170000076293945,3.7639999389648438,5.070000171661377,5.074999809265137,5.738999843597412,6.72599983215332,7.739999771118164,8.072999954223633,8.817999839782715,7.611000061035156,11.229999542236328,11.770000457763672,12.949999809265137,14.649999618530273,13.880000114440918,14.789999961853027,16.079999923706055,16.690000534057617,15.869999885559082,14.760000228881836,16.139999389648438,18.06999969482422,14.270000457763672,13.069999694824219,11.680000305175781,10.100000381469727,9.79699993133545,8.996999740600586,6.681000232696533,5.531000137329102,6.395999908447266,6.09499979019165,3.506999969482422,3.319999933242798,2.3269999027252197,3.1600000858306885,2.703000068664551,2.872999906539917,4.584000110626221,3.8919999599456787,4.140999794006348,4.188000202178955,3.6630001068115234,3.8589999675750732,4.486999988555908,4.204999923706055,4.3520002365112305,3.303999900817871,3.365999937057495,3.371000051498413,2.6010000705718994,2.5920000076293945,3.0199999809265137]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"inflation (t+1) output plot\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[1,57],\"title\":{\"text\":\"Time step\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,20.005],\"title\":{\"text\":\"inflation\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>inflation (t+1) output plot. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-inflationprediction-t5-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-inflationprediction-t5-s2-20261006\" role=\"img\" aria-label=\"inflation (t+2) output plot\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-inflationprediction-t5-s2-20261006.png\" alt=\"inflation (t+2) output plot\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Targets\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],\"y\":[4.400000095367432,5.099999904632568,4.599999904632568,4.099999904632568,4.699999809265137,5,5.599999904632568,6.400000095367432,7.400000095367432,8,8.699999809265137,8.100000381469727,10.199999809265137,11.399999618530273,12.800000190734863,14.199999809265137,14.199999809265137,14.800000190734863,15.699999809265137,16.399999618530273,16.100000381469727,15.300000190734863,15.899999618530273,17.200000762939453,15.199999809265137,14,12.5,11,10.300000190734863,9.5,7.699999809265137,6.300000190734863,6.300000190734863,6.199999809265137,4.5,3.700000047683716,2.700000047683716,3,2.799999952316284,2.9000000953674316,4,4,4.199999809265137,4.199999809265137,3.9000000953674316,3.9000000953674316,4.300000190734863,4.300000190734863,4.400000095367432,3.700000047683716,3.5,3.4000000953674316,2.9000000953674316,2.700000047683716,2.9000000953674316,2.9000000953674316,2.5999999046325684]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Outputs\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],\"y\":[3.614000082015991,3.4769999980926514,4.665999889373779,5.245999813079834,4.013000011444092,3.6619999408721924,4.979000091552734,4.923999786376953,5.5920000076293945,6.550000190734863,7.526000022888184,7.816999912261963,8.559000015258789,7.307000160217285,11.050000190734863,11.4399995803833,12.649999618530273,14.399999618530273,13.529999732971191,14.520000457763672,15.869999885559082,16.489999771118164,15.59000015258789,14.420000076293945,15.930000305175781,17.979999542236328,13.850000381469727,12.6899995803833,11.289999961853027,9.711000442504883,9.470999717712402,8.666999816894531,6.370999813079834,5.322999954223633,6.232999801635742,5.888999938964844,3.3299999237060547,3.249000072479248,2.2639999389648438,3.131999969482422,2.6410000324249268,2.8310000896453857,4.5289998054504395,3.765000104904175,4.046999931335449,4.081999778747559,3.558000087738037,3.7739999294281006,4.389999866485596,4.084000110626221,4.24399995803833,3.194999933242798,3.299999952316284,3.2990000247955322,2.5339999198913574,2.555000066757202,2.9800000190734863]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"inflation (t+2) output plot\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[1,57],\"title\":{\"text\":\"Time step\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,20.005],\"title\":{\"text\":\"inflation\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>inflation (t+2) output plot. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-inflationprediction-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Economic regimes and policy changes can alter the relationship. Adjacent forecast windows are dependent; future evaluation should roll the origin forward and fit preprocessing only on the training period. A persistence baseline should accompany an operational comparison. The saved testing roles and report figures are the evidence for this run.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/ec.europa.eu\/eurostat\/databrowser\/view\/prc_hicp_manr\/default\/table\">HICP &#8211; monthly data (annual rate of change): Poland, all-items<\/a>. European Commission, Eurostat. 10.2908\/prc_hicp_manr<\/li><li>Dataset terms: Eurostat copyright notice and free re-use of data policy. Full attribution and transformations are included in <code>LICENSES\/DATASET-LICENSE.txt<\/code>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-inflationprediction-licensed-20261006.zip\">Current Neural Designer project and saved task report<\/a>, snapshot 6 October 2026.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<style>\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 auto!important;width:100%;height:auto;max-width:100%}\n.nd-chart-bundle>script,.nd-chart-bundle>br,.nd-chart-bundle>p:empty{display:none!important}\n@media(max-width:600px){.nd-chart-bundle{margin:20px auto}.nd-chart-bundle .nd-chart-host{height:440px}}\n.nd-chart-bundle .nd-chart-host{min-width:0!important}.nd-chart-bundle{max-width:100%}html body .nd-comparison-grid>.nd-comparison-item:only-child{flex-basis:min(760px,100%)!important;width:min(760px,100%)!important}.ndb-card details,.nds-card details{margin:20px 0}.ndb-card summary,.nds-card summary{cursor:pointer;font-weight:600}<\/style><script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":13,"featured_media":2391,"template":"","categories":[29],"tags":[47],"class_list":["post-3502","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-finance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Forecasting monthly inflation in Poland<\/title>\n<meta name=\"description\" content=\"Use 12 monthly observations to forecast the next three observations of Poland\u2019s all-items HICP annual rate of change. The source contains 348 monthly values from January 1997 through December 2025.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/inflation-prediction\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Inflation prediction machine learning example\" \/>\n<meta property=\"og:description\" content=\"Inflation is the rate of increase in the cost of goods and benefits over a given period of time. Core inflation excludes food and energy prices because of their variability. This example aims to predict core inflation from the macroeconomic data of a country.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/inflation-prediction\/\" \/>\n<meta property=\"og:site_name\" content=\"Neural Designer\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-06T11:26:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/core_inflation-scaled.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1340\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Inflation prediction machine learning example\" \/>\n<meta name=\"twitter:description\" content=\"Inflation is the rate of increase in the cost of goods and benefits over a given period of time. Core inflation excludes food and energy prices because of their variability. 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