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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>Forecasting a week of retail sales<\/h2>\n<p>Inventory and staffing plans need a view of demand before the next trading week. This example uses seven days of recorded retail activity to forecast daily sales for the following seven days, then compares the forecasts with simple historical baselines.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>942<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>70<\/strong><span>Values in each input window<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>176<\/strong><span>Test forecast origins<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.781<\/strong><span>Day +1 test prediction R\u00b2<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Explore the model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/store-sales-data-20260921.csv\">Download the data<\/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>The operational question is how much aggregate sales activity to expect over the next week. A useful comparison measures forecast errors in the same units and on the same dates as a simple reference forecast.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Seven-day history<\/h3><p>Use the recorded calendar, sales, customer, opening and promotional fields from the previous week.<\/p><\/div><div class=\"ndb-value\"><h3>Seven-day forecast<\/h3><p>Produce one sales estimate for each day of the following week.<\/p><\/div><div class=\"ndb-value\"><h3>Planning evidence<\/h3><p>Compare errors with repeating the previous week; forecasting accuracy does not establish a promotion\u2019s causal effect.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Demand planning<\/span><span>Retail operations<\/span><span>Forecast evaluation<\/span><\/div><div class=\"ndb-note\">This is an internal historical benchmark. The chronological windows do not cross subset boundaries, but the native preprocessing statistics use the saved used-row set, including later periods. A prospective evaluation must fit preprocessing within training and roll the forecast origin forward. Overlapping test windows are dependent. Results do not establish transfer to a different store, future period or promotional policy.<\/div><\/section>\n<span id=\"DataAnalysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><h3>Seasonal patterns in the source series<\/h3><p>The saved Neural Designer temporal means summarize all 942 daily records by calendar month, day of month and weekday. Each value is mean daily sales in the source units, not a monthly total. These descriptive averages cover the full source period; they are separate from the held-out forecast evaluation below.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-temporal-month-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-temporal-month-20260921\" role=\"img\" aria-label=\"sales mean by month\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales mean by month. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],\"y\":[5149490,5324120,5455620,5417710,5175440,5433190,5692580,5345310,5246280,5195190,5656810,6429550]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"sales mean by month\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Month\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,7001000],\"title\":{\"text\":\"sales\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Mean daily sales by calendar month. Native Neural Designer export.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-temporal-month-day-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-temporal-month-day-20260921\" role=\"img\" aria-label=\"sales mean by day of month\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales mean by day of month. Enable JavaScript to explore it.<\/p><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\":\"sales\",\"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],\"y\":[4537840,6579660,5480700,6486230,5991830,5629930,5606440,5343450,4916670,5089970,5151310,4898420,5385970,5034130,5578140,5984970,5779320,6028230,5500080,5487350,5090370,5174520,5316860,4681200,4531590,4528440,5439180,5509750,5586410,6869390,5818540]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"sales mean by day of month\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[1,31],\"title\":{\"text\":\"Day of month\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,8001000],\"title\":{\"text\":\"sales\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Mean daily sales by day of month. Native Neural Designer export.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-temporal-weekday-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-temporal-weekday-20260921\" role=\"img\" aria-label=\"sales mean by day of week\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales mean by day of week. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\",\"Sun\"],\"y\":[7395890,6636860,6184940,5929310,6321760,5390800,215530]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"sales mean by day of week\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\",\"Sun\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Day of week\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,8002000],\"title\":{\"text\":\"sales\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Mean daily sales by weekday. Native Neural Designer export.<\/figcaption><\/figure><h3>Chronological evaluation<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Source segment<\/th><th scope=\"col\">First date<\/th><th scope=\"col\">Last date<\/th><th scope=\"col\">Valid 14-day windows<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>01\/01\/2013<\/td><td>19\/07\/2014<\/td><td>552<\/td><\/tr><tr><th scope=\"row\">Validation<\/th><td>20\/07\/2014<\/td><td>23\/01\/2015<\/td><td>175<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>24\/01\/2015<\/td><td>31\/07\/2015<\/td><td>176<\/td><\/tr><\/tbody><\/table><\/div><p>The prepared Rossmann table contains 942 daily records from 1 January 2013 to 31 July 2015. It is an aggregate retail series: there is one row per date, no store identifier, and the open and customers fields contain daily counts. The model therefore forecasts the supplied aggregate sales series, rather than a specific individual store. Sales retain the source file units; the prepared CSV does not label a currency.<\/p><p>The downloadable project, saved report and supplied source CSV define the exact version used here. Repository: <a href=\"https:\/\/www.kaggle.com\/c\/rossmann-store-sales\/data\">original dataset\/source record<\/a>. <\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">Records<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>552<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>175<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>176<\/td><\/tr><tr><th scope=\"row\">Unused<\/th><td>39<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Variable<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Encoding<\/th><th scope=\"col\">Unit<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">day<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>day of month<\/td><\/tr><tr><th scope=\"row\">month<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>month<\/td><\/tr><tr><th scope=\"row\">year<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>year<\/td><\/tr><tr><th scope=\"row\">day_of_week<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>1=Monday; 7=Sunday<\/td><\/tr><tr><th scope=\"row\">open<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>daily count<\/td><\/tr><tr><th scope=\"row\">sales<\/th><td>InputTarget<\/td><td>Numeric<\/td><td><\/td><td>source sales units<\/td><\/tr><tr><th scope=\"row\">customers<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>daily count<\/td><\/tr><tr><th scope=\"row\">promo<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0 or 1<\/td><\/tr><tr><th scope=\"row\">state_holiday<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0 or 1<\/td><\/tr><tr><th scope=\"row\">school_holiday<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>0 or 1<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t0-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t0-s1-20260921\" role=\"img\" aria-label=\"sales distribution\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales distribution. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"showlegend\":false,\"type\":\"bar\",\"x\":[\"427000\",\"1090000\",\"1750000\",\"2420000\",\"3080000\",\"3750000\",\"4410000\",\"5070000\",\"5740000\",\"6400000\",\"7070000\",\"7730000\",\"8390000\",\"9060000\",\"9720000\",\"1.04e+07\",\"1.10e+07\",\"1.17e+07\",\"1.24e+07\",\"1.30e+07\"],\"y\":[16.899999618530273,0,0,0,0.20000000298023224,0.6000000238418579,2,22.899999618530273,13.399999618530273,11.199999809265137,13.600000381469727,6.099999904632568,4.400000095367432,4.300000190734863,2.4000000953674316,1.100000023841858,0.4000000059604645,0,0.20000000298023224,0.10000000149011612]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"sales histogram\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"categoryarray\":[\"427000\",\"1090000\",\"1750000\",\"2420000\",\"3080000\",\"3750000\",\"4410000\",\"5070000\",\"5740000\",\"6400000\",\"7070000\",\"7730000\",\"8390000\",\"9060000\",\"9720000\",\"1.04e+07\",\"1.10e+07\",\"1.17e+07\",\"1.24e+07\",\"1.30e+07\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"title\":{\"text\":\"Bin center\"},\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,40.01],\"title\":{\"text\":\"% frequency\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>sales distribution. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><div class=\"ndb-note\">Windows use chronological segments: 552 training origins, 175 validation origins and 176 testing origins. Thirteen starts per segment are excluded so their 14-day input-and-target windows cannot cross a boundary or the end of the series.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The final model uses <strong>10 features per day across 7 days<\/strong> (70 values) and produces <strong>7 daily sales outputs<\/strong>. The following dimensions describe the final saved network.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><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>7 10<\/td><td>7 10<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Recurrent<\/th><td>7 10<\/td><td>9<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>9<\/td><td>7<\/td><td>Identity<\/td><\/tr><tr><th scope=\"row\">Unscaling<\/th><td>7<\/td><td>7<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Clamping<\/th><td>7<\/td><td>7<\/td><td>\u2014<\/td><\/tr><\/tbody><\/table><\/div><p>The final output is a continuous estimate. Scaling, unscaling and any bounds follow the saved implementation; the calculator does not silently impose physical constraints.<\/p><figure class=\"ndb-architecture-figure\" data-model-stage=\"initial\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-store-sales-t1-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-store-sales-t1-s1-20260921.png\" alt=\"Forecasting a week of retail sales: initial Neural Designer architecture\"><\/a><figcaption>Initial architecture before selection. The recurrent block processes the seven-day sequence; the output layer produces the next seven daily sales values.<\/figcaption><\/figure><\/section>\n<span id=\"ModelTraining\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>Training uses normalized squared error, L2 regularization (weight 0.01) and the Quasi-Newton optimizer. Training minimizes the recorded objective; the validation subset monitors generalization during fitting. The testing subset is used for the evaluation below.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t2-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t2-s2-20260921\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Quasi-Newton method error history. Enable JavaScript to explore it.<\/p><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\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149],\"y\":[1.5099999904632568,1.100000023841858,0.9490000009536743,0.8420000076293945,0.7950000166893005,0.7400000095367432,0.6949999928474426,0.6510000228881836,0.5989999771118164,0.5699999928474426,0.531000018119812,0.5099999904632568,0.48500001430511475,0.4729999899864197,0.4580000042915344,0.44999998807907104,0.43700000643730164,0.4300000071525574,0.42500001192092896,0.4189999997615814,0.41499999165534973,0.4090000092983246,0.4059999883174896,0.40299999713897705,0.4020000100135803,0.4000000059604645,0.3959999978542328,0.39399999380111694,0.38999998569488525,0.3889999985694885,0.3880000114440918,0.38600000739097595,0.38600000739097595,0.38499999046325684,0.3840000033378601,0.382999986410141,0.382999986410141,0.38199999928474426,0.38199999928474426,0.38199999928474426,0.38199999928474426,0.38199999928474426,0.38100001215934753,0.38100001215934753,0.3799999952316284,0.3799999952316284,0.3790000081062317,0.3790000081062317,0.3790000081062317,0.3790000081062317,0.3779999911785126,0.3779999911785126,0.3779999911785126,0.3779999911785126,0.3779999911785126,0.3779999911785126,0.37700000405311584,0.37700000405311584,0.37700000405311584,0.37700000405311584,0.37700000405311584,0.37599998712539673,0.37599998712539673,0.375,0.375,0.375,0.37400001287460327,0.37400001287460327,0.37299999594688416,0.37299999594688416,0.3720000088214874,0.3720000088214874,0.3709999918937683,0.3709999918937683,0.3700000047683716,0.36899998784065247,0.36899998784065247,0.36800000071525574,0.36899998784065247,0.36800000071525574,0.367000013589859,0.367000013589859,0.3659999966621399,0.3659999966621399,0.36500000953674316,0.36399999260902405,0.36500000953674316,0.36500000953674316,0.36500000953674316,0.36500000953674316,0.36500000953674316,0.36500000953674316,0.36399999260902405,0.36399999260902405,0.36399999260902405,0.36399999260902405,0.3630000054836273,0.3630000054836273,0.36399999260902405,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3630000054836273,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3619999885559082,0.3630000054836273,0.3630000054836273,0.3630000054836273]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149],\"y\":[1.2400000095367432,1.059999942779541,0.9259999990463257,0.8820000290870667,0.8460000157356262,0.8169999718666077,0.7710000276565552,0.7089999914169312,0.6759999990463257,0.6290000081062317,0.6119999885559082,0.6110000014305115,0.609000027179718,0.6019999980926514,0.5820000171661377,0.5730000138282776,0.5590000152587891,0.5490000247955322,0.5389999747276306,0.5320000052452087,0.5260000228881836,0.5210000276565552,0.5180000066757202,0.5139999985694885,0.5059999823570251,0.49900001287460327,0.4950000047683716,0.48899999260902405,0.48899999260902405,0.4860000014305115,0.48399999737739563,0.48100000619888306,0.4790000021457672,0.47999998927116394,0.47999998927116394,0.4819999933242798,0.4830000102519989,0.4830000102519989,0.4819999933242798,0.47999998927116394,0.4779999852180481,0.4779999852180481,0.4779999852180481,0.4790000021457672,0.4790000021457672,0.4790000021457672,0.4790000021457672,0.4779999852180481,0.4779999852180481,0.4779999852180481,0.4779999852180481,0.4779999852180481,0.47999998927116394,0.47999998927116394,0.48100000619888306,0.48100000619888306,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.4830000102519989,0.4830000102519989,0.4830000102519989,0.48100000619888306,0.48100000619888306,0.48100000619888306,0.47999998927116394,0.47999998927116394,0.47999998927116394,0.48100000619888306,0.47999998927116394,0.48399999737739563,0.4819999933242798,0.4819999933242798,0.4830000102519989,0.4830000102519989,0.48500001430511475,0.4860000014305115,0.4869999885559082,0.48899999260902405,0.48899999260902405,0.4909999966621399,0.492000013589859,0.4909999966621399,0.48899999260902405,0.4869999885559082,0.48500001430511475,0.48500001430511475,0.4860000014305115,0.4869999885559082,0.4880000054836273,0.48899999260902405,0.48899999260902405,0.48899999260902405,0.4880000054836273,0.4880000054836273,0.4860000014305115,0.4860000014305115,0.4860000014305115,0.48500001430511475,0.48500001430511475,0.48500001430511475,0.48500001430511475,0.4860000014305115,0.4869999885559082,0.4869999885559082,0.4869999885559082,0.48500001430511475,0.48500001430511475,0.48399999737739563,0.48500001430511475,0.48500001430511475,0.4860000014305115,0.48500001430511475,0.48500001430511475,0.48500001430511475,0.48500001430511475,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48500001430511475,0.48500001430511475,0.48500001430511475,0.48500001430511475,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.48399999737739563,0.4830000102519989,0.4830000102519989,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.4819999933242798,0.48100000619888306,0.48100000619888306,0.48100000619888306]}],\"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\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Quasi-Newton method error history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,149],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,2.0005],\"title\":{\"text\":\"Normalized squared error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Quasi-Newton method error history. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>150<\/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>0.379<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.477<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>The initial model has 5 recurrent neurons. The saved growing-neurons search evaluates 1\u201310 neurons with three trials and retains 9 neurons using validation error.<\/p><p>The following search results are recorded in the saved report. Selection used the validation subset. These are the selected settings within the tested search; they do not establish a globally optimal model.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t3-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t3-s2-20260921\" role=\"img\" aria-label=\"Growing neurons training\/selection errors plot\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Growing neurons training\/selection errors plot. Enable JavaScript to explore it.<\/p><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},\"marker\":{\"color\":\"#529cc2\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10],\"y\":[0.8583890199661255,0.7087889909744263,0.5919349789619446,0.4619010090827942,0.3609740138053894,0.26183098554611206,0.21350300312042236,0.2136009931564331,0.20629100501537323,0.20592400431632996]},{\"connectgaps\":false,\"line\":{\"color\":\"#e6cd5f\",\"dash\":\"solid\",\"width\":2},\"marker\":{\"color\":\"#e6cd5f\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Selection error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10],\"y\":[0.9801629781723022,0.8196920156478882,0.7019309997558594,0.5770009756088257,0.4647369980812073,0.35182398557662964,0.3242909908294678,0.3239009976387024,0.32321399450302124,0.3248029947280884]}],\"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\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Growing neurons training\/selection errors plot\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[1,10],\"title\":{\"text\":\"Neurons number\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"Normalized squared error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Growing neurons training\/selection errors plot. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Growing neurons results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Optimal neurons number<\/th><td>9<\/td><\/tr><tr><th scope=\"row\">Optimum training error<\/th><td>0.2063<\/td><\/tr><tr><th scope=\"row\">Optimum selection error<\/th><td>0.3232<\/td><\/tr><tr><th scope=\"row\">Epochs number<\/th><td>10<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum neurons reached<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:11<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"ndb-architecture-figure\" data-model-stage=\"selected\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-store-sales-t3-s5-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-store-sales-t3-s5-20260921.png\" alt=\"Forecasting a week of retail sales: selected Neural Designer architecture\"><\/a><figcaption>Final architecture used for testing. The recurrent block processes the seven-day sequence; the output layer produces the next seven daily sales values.<\/figcaption><\/figure><\/section>\n<span id=\"TestingAnalysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The final model is evaluated at <strong>176 testing forecast origins<\/strong>, each producing seven future daily values. MAE, RMSE and prediction R\u00b2 below are computed from the exact testing pairs stored by Neural Designer. Errors retain each target\u2019s original units.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Horizon<\/th><th scope=\"col\">MAE (millions)<\/th><th scope=\"col\">RMSE (millions)<\/th><th scope=\"col\">Prediction R\u00b2<\/th><th scope=\"col\">Previous-week MAE (millions)<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Day +1<\/th><td>0.723<\/td><td>1.274<\/td><td>0.781<\/td><td>1.894<\/td><\/tr><tr><th scope=\"row\">Day +2<\/th><td>0.739<\/td><td>1.289<\/td><td>0.780<\/td><td>1.886<\/td><\/tr><tr><th scope=\"row\">Day +3<\/th><td>0.743<\/td><td>1.314<\/td><td>0.769<\/td><td>1.902<\/td><\/tr><tr><th scope=\"row\">Day +4<\/th><td>0.757<\/td><td>1.296<\/td><td>0.774<\/td><td>1.909<\/td><\/tr><tr><th scope=\"row\">Day +5<\/th><td>0.722<\/td><td>1.312<\/td><td>0.768<\/td><td>1.918<\/td><\/tr><tr><th scope=\"row\">Day +6<\/th><td>0.738<\/td><td>1.310<\/td><td>0.769<\/td><td>1.927<\/td><\/tr><tr><th scope=\"row\">Day +7<\/th><td>0.796<\/td><td>1.369<\/td><td>0.749<\/td><td>1.944<\/td><\/tr><\/tbody><\/table><\/div><p>The native goodness-of-fit task reports squared Pearson correlation (r\u00b2). Prediction R\u00b2 here is 1 \u2212 squared prediction error \/ squared deviation from the test mean. They measure different properties: a strongly correlated prediction can still have a bias or an incorrect scale.<\/p><h3>Historical baselines<\/h3><p>The weekly baseline repeats the seven input sales values as the next week. Persistence repeats the latest observed sales value for all seven horizons. All three use the same test origins and target dates. The table reports averages over the seven horizons, in millions of the CSV sales units.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Forecast<\/th><th scope=\"col\">Mean MAE (millions)<\/th><th scope=\"col\">Mean RMSE (millions)<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Recurrent model<\/th><td>0.745<\/td><td>1.309<\/td><\/tr><tr><th scope=\"row\">Repeat the previous week<\/th><td>1.912<\/td><td>2.710<\/td><\/tr><tr><th scope=\"row\">Repeat the latest observed day<\/th><td>2.915<\/td><td>3.886<\/td><\/tr><\/tbody><\/table><\/div><p>The first and seventh horizons are shown below. The download includes native interactive charts for every intermediate horizon.<\/p><div class=\"nd-native-test-figures\"><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t4-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t4-s2-20260921\" role=\"img\" aria-label=\"sales (t+1) goodness-of-fit chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales (t+1) goodness-of-fit chart. Enable JavaScript to explore it.<\/p><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 sales (t+1)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[6082736,217254,8866293,7553617,6885692,6771608,7124966,5408921,200610,5485944,5334565,5400184,5154777,6077650,5508550,206940,7694543,7220097,6862881,6819636,6838205,5194592,192186,5020984,5098889,5112257,5503071,6636509,6168295,220542,9371535,7816601,7151261,7054135,7176780,5411869,193306,5720780,5275102,5300898,5388152,5850465,5454800,236825,9242779,7458887,6776155,6856032,6999556,5227549,228181,5714616,5277385,5112983,5352141,5923528,5602478,219567,10450233,9592251,9553230,9797036,233627,7841647,209231,264170,6675161,5750977,5558433,5620983,5281460,237910,9421242,7783392,6786189,7147406,7396904,5358034,229887,5556755,5114098,5151843,5171605,5342682,5180053,227480,9021056,7957790,8047815,9630001,252402,7217531,241572,9378919,7493543,7356622,7268682,7349331,5861396,243430,5694201,5698170,6893084,280131,7201204,5343304,248127,9023210,7556007,7095093,6906628,6951103,5496582,252807,288354,6556576,5808700,5959180,7191541,6279580,270405,10079151,8475455,8247024,3518788,8079846,5361879,254614,6215970,5768461,5493978,5552838,5586714,5349939,261441,9850343,8144121,7300181,6948660,7103743,5241792,241981,5476627,5292172,5300565,5240987,5572151,5352198,255419,10068218,9721603,7756494,7227424,7222894,4980051,259708,6439824,5483482,5872715,5893315,5975483,5180183,268279,9211885,7932107,7467342,7151216,6688440,5289438,289944,6163686,5377207,5174305,5566029,5509371,4831061],\"y\":[5001788,109906,6101970.5,6593261,6142155.5,6660446.5,6581770,5739684,538240,5972868.5,5193224.5,5070163,4661250,6228059,5630006,1105881,8631762,8194407,7083073,6690192.5,6700748.5,5272872,320925,5680453.5,4945312,4889660.5,4482970,5953009,5518984.5,1110053,8727808,7978637,7347065,7364828,6962306,5769158.5,509482,5996896.5,5187581,5109182,4708302,6252998.5,5675927,1161961,8640950,7969470,7183885,6957076.5,6795597.5,5263869.5,277516.5,5772756.5,4943131,4938177,4544141,6019418.5,5554549,1116929.5,8516934,7754955.5,7286569,6995022,6903507.5,8317439,1387483.25,4421179,6556530,5177376,4108227,5778568,5402489.5,1066928.5,8431155,7789863,7141038,6899293.5,6799609,5299477,281871,5908735,4999903.5,5003265.5,4588319.5,6083031.5,5601573,1123632,8537078,7962663,7257055.5,6700818.5,6737269,7641679,559709.5,5209928,7227846.5,6443961.5,6201031,6464339.5,5541695,419118.5,6039750,5257161.5,5157563,4702637.5,6911964,5583315.5,684725,8841285,8038547,6981140,6516622,6792194,5393847,171574,5840459.5,5914043,4848146,4465696,6076956,5609442.5,1250139.25,8404644,8133244,7688954.5,7614540,7017911,5692428,297764,6451914,5391664.5,4863991,4981417.5,6423797,5810321,1270100.75,8736617,8060673.5,7439902,7138648.5,6948790,5508083.5,218604,5961490.5,5033878,5058978.5,4625485.5,6099841,5667728.5,1207288,8581029,7965622,7577884.5,7352245,7274181,6142380,421678.5,6198239,5258498.5,5297951.5,4869178.5,6434629.5,5882575.5,1357571,8804889,8175258.5,7427801,7051507,6948942,5638157.5,266364.5,5940530,5079426.5,5148606,4708164,6164957,5727050]},{\"connectgaps\":false,\"line\":{\"color\":\"#3c3c3c\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Predicted sales (t+1)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,14002000],\"y\":[0,14002000]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"sales (t+1) goodness-of-fit chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-280040,14282040],\"title\":{\"text\":\"Actual sales (t+1)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-280040,14282040],\"title\":{\"text\":\"Predicted sales (t+1)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>sales (t+1) goodness-of-fit chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t4-s14-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t4-s14-20260921\" role=\"img\" aria-label=\"sales (t+7) goodness-of-fit chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales (t+7) goodness-of-fit chart. Enable JavaScript to explore it.<\/p><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 sales (t+7)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[7124966,5408921,200610,5485944,5334565,5400184,5154777,6077650,5508550,206940,7694543,7220097,6862881,6819636,6838205,5194592,192186,5020984,5098889,5112257,5503071,6636509,6168295,220542,9371535,7816601,7151261,7054135,7176780,5411869,193306,5720780,5275102,5300898,5388152,5850465,5454800,236825,9242779,7458887,6776155,6856032,6999556,5227549,228181,5714616,5277385,5112983,5352141,5923528,5602478,219567,10450233,9592251,9553230,9797036,233627,7841647,209231,264170,6675161,5750977,5558433,5620983,5281460,237910,9421242,7783392,6786189,7147406,7396904,5358034,229887,5556755,5114098,5151843,5171605,5342682,5180053,227480,9021056,7957790,8047815,9630001,252402,7217531,241572,9378919,7493543,7356622,7268682,7349331,5861396,243430,5694201,5698170,6893084,280131,7201204,5343304,248127,9023210,7556007,7095093,6906628,6951103,5496582,252807,288354,6556576,5808700,5959180,7191541,6279580,270405,10079151,8475455,8247024,3518788,8079846,5361879,254614,6215970,5768461,5493978,5552838,5586714,5349939,261441,9850343,8144121,7300181,6948660,7103743,5241792,241981,5476627,5292172,5300565,5240987,5572151,5352198,255419,10068218,9721603,7756494,7227424,7222894,4980051,259708,6439824,5483482,5872715,5893315,5975483,5180183,268279,9211885,7932107,7467342,7151216,6688440,5289438,289944,6163686,5377207,5174305,5566029,5509371,4831061,277960,8972089,7616689,7097759,7349475,8547151],\"y\":[5520178,5608715.5,475765.5,6430563,5594428,5164465,4743880.5,5154117,5556859,528328,8482487,7679817,7026564,6387821.5,6953848.5,4833131.5,470054,5943866,5462835.5,5211665,4939659.5,5409544,5714529.5,723529,8644461,7781782,7107438,6581752.5,6973245,4877698,459183,6063371,5244330.5,5052285,4881159,5214635,5596611.5,596388.5,8545152,7715603,7052825,6453029,6963246,4850285,533056,6131738,5416195.5,5208660,5018249.5,5477169.5,5692697,806468,8704622,7826981.5,7124001,6613007,6995074,4930182.5,619476.5,6008768,5018175.5,5107863,4071712,4610963.5,6189772,915313,7348412.5,7617102.5,7077235.5,6178605.5,6777412,4939554.5,465670.5,6419965.5,5594351.5,5188300,5016581.5,5505033,5692296,791594.5,8742650,7851177,7161579.5,6626031.5,7016225.5,4968809.5,637031,6305448,5598557,5457216.5,5091629.5,4955003,6178964.5,839211.5,6467705.5,5976968.5,5489391.5,4814004,5435408,5693470.5,695916,8648205,7856133,7205476,5850723,7180643.5,5059925,590712,6404131,5757534.5,5255371.5,5095724,5612381,5804137.5,900839,8274950.5,8070779,7220268.5,6698796,7080469.5,4957034,712699.5,6156486.5,5227318.5,5155108.5,5639693,5562531,5393021.5,758577,8747925,7923222.5,7058616,6592605,7062359.5,4958744.5,660172,6309805.5,5513278.5,5344050,5114754.5,5616147.5,5799054,944083.5,8854168,7966087,7252312,6748991.5,7094162.5,5040042.5,747165.5,6506254.5,5656408,5292664.5,4872458,5410339.5,5531304,815588,8787471,7889882.5,7198344,6646137,7095923.5,4965000.5,705065.5,6317079.5,5610165.5,5403335,5142678,5622256,5860444,976935,8867206,7993191,7263384.5,6791972.5,7114243]},{\"connectgaps\":false,\"line\":{\"color\":\"#3c3c3c\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Predicted sales (t+7)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,14002000],\"y\":[0,14002000]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"sales (t+7) goodness-of-fit chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-280040,14282040],\"title\":{\"text\":\"Actual sales (t+7)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-280040,14282040],\"title\":{\"text\":\"Predicted sales (t+7)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>sales (t+7) goodness-of-fit chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t6-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t6-s1-20260921\" role=\"img\" aria-label=\"sales (t+1) output plot\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales (t+1) output plot. 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Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-store-sales-t6-s7-20260921\"><div class=\"nd-chart-host\" id=\"nd-store-sales-t6-s7-20260921\" role=\"img\" aria-label=\"sales (t+7) output plot\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: sales (t+7) output plot. Enable JavaScript to explore it.<\/p><script 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Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><\/div><\/section>\n<span id=\"ModelDeployment\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>The JavaScript adapter keeps the generated arithmetic and exposes all seven computed outputs. The original HTML export exposed only the final horizon. The supplied inference.js is checked against every native test window; use it with the documented input order for batch forecasts.<\/p><p>Check seven days of inputs \u2192 apply the saved preprocessing and recurrent network \u2192 forecast the following seven days \u2192 review forecast errors and operating context. The ZIP contains the original project, source CSV, schema, test metrics and standalone interactive chart exports. The project hash in the schema identifies this exact version.<\/p><p>The model takes a 7 \u00d7 10 input window. For repeatable use, run the project in Neural Designer or call predict(inputs) from the downloadable inference.js. The input schema specifies the day-by-day order, from the oldest day to the latest; outputs are the next seven daily sales values.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/store-sales-neural-designer-temporal-means-20260921.zip\">Download project and exports (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/store-sales-data-20260921.csv\">Download source data (CSV)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/store-sales-schema-temporal-means-20260921.zip\">Download input schema (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Reproduce with Neural Designer<\/a><\/div><\/section>\n<span id=\"Conclusions\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>This is an internal historical benchmark. The chronological windows do not cross subset boundaries, but the native preprocessing statistics use the saved used-row set, including later periods. A prospective evaluation must fit preprocessing within training and roll the forecast origin forward. Overlapping test windows are dependent. Results do not establish transfer to a different store, future period or promotional policy.<\/p><p>No external validation or independent calibration study is included. Preprocessing statistics and model choices should be refitted within a prospective or grouped validation design. Correlations and directional responses describe associations, not causes. Human review is required before an operational decision.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/www.kaggle.com\/c\/rossmann-store-sales\/data\">Dataset source and provenance<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/store-sales-neural-designer-temporal-means-20260921.zip\">Neural Designer project and saved task evidence, 21 September 2026<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">Neural Designer testing-analysis documentation<\/a>.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<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 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