数据为Impression CPC195212 069673883101838 08964356415084 086552926 10489010938715 122744525108302 0738867421040 134617647154213 1035572645684 05835456 07137839068 079777727001 057666624973 06356333 0477

在使用指数衰减模型进行分析之前，首先需要对数据进行预处理，将CPC价格作为竞价的影响因素，然后使用指数衰减模型来描述广告的位置和商品的季节性对曝光次数的影响。

在该模型中，曝光次数可以表示为竞价的指数函数，即：

曝光次数 = A * exp(-B * CPC)

其中，A和B是待求的参数。

以下是使用MATLAB实现该模型的代码：

% 数据预处理
impression = [195212; 101838; 15084; 52926; 38715; 108302; 21040; 154213; 5684; 35456; 9068; 27001; 24973; 6333; 1537; 124851; 44952; 15168; 10652; 46720; 30230; 217830; 79198; 68764; 27510; 31848; 28369; 6434; 8987; 29389; 13683; 29692; 46833; 17746; 68184; 13168; 117; 108035; 102197; 92610; 4259; 22207; 79830; 60892; 163592; 45020; 79884; 99699; 305993; 137698; 97951; 83809; 15494; 40400; 338448; 246330; 123139; 120427; 44911; 105031; 327173; 473857; 413684; 141090; 202715; 106463; 52957; 766606; 818594; 1649165; 25; 1982; 8688; 5316; 2139; 3123; 36369; 20563; 14410; 3484; 4665; 15248; 15775; 7493; 18485; 17379; 5969; 4373; 1432; 17821; 56619; 232562; 496424; 3340; 3250; 781; 1687; 19957; 50484; 24167; 40594; 73590; 112284; 97698; 323397; 416162; 493754; 787270; 848611; 981722; 683244; 753737; 946337; 436238; 16274; 47703; 30549; 65356; 132106; 300245; 223688; 235671; 149082; 107264; 275612; 254804; 108922; 90214; 587109; 854018; 898178; 67412; 1566977; 2866360; 2374888; 1963732; 1242897; 594129; 367181; 239316; 253221; 273653; 470871; 416611; 337016; 254602; 314961; 249649; 2972; 21716; 21333; 15386; 39453; 45840; 30359; 470960; 467347; 493582; 885566; 771369; 461835; 415336; 405741; 229283; 160912; 69; 967; 1034; 1996; 2914; 1517; 1817; 50404; 51353; 37555; 69370; 112830; 64800; 45351; 71407; 67901; 64024; 103755; 230215; 82574; 157893; 231862; 39995; 25129; 54700; 24903; 19052; 22101; 1883; 5983; 87323; 65952; 407398; 325556; 220664; 289929; 254592; 90523; 43140; 115055; 8578; 64606; 19920; 21101; 49057; 34079];
cpc = [0.69673883; 0.89643564; 0.865; 1.04890109; 1.22744525; 0.7388674; 1.34617647; 1.03557264; 0.58; 0.713783; 0.797777; 0.576666; 0.635; 0.4775; 0.96333333; 0.82841216; 0.99886178; 0.86548387; 1.45714285; 1.72097014; 1.13644067; 1.43767058; 1.43487632; 0.605794; 0.585937; 0.625692; 0.51137931; 0.344; 0.56625; 0.72235294; 0.82866666; 1.17482758; 0.9717; 0.65454545; 0.77363636; 0.75333333; 0; 0.7704; 0.93690537; 0.73411931; 0.835; 0.770781; 0.81315789; 0.62209302; 0.62374787; 0.80254098; 0.75581673; 0.54994791; 0.93278179; 0.48574906; 0.53612293; 0.71178571; 0.41933333; 0.51608695; 0.83829787; 0.6924791; 0.89077464; 0.65660606; 0.78472972; 0.714581; 1.4980888; 1.88519924; 2.51165625; 1.94143835; 0.71928709; 0.69798816; 0.9128169; 0.85488768; 0.80572428; 0.85083133; 0; 1.328; 1.05525423; 1.33; 1.509375; 1.015; 1.74751773; 1.36279069; 0.95981481; 0.8; 0.899375; 1.61888888; 2.2825; 3.41666666; 1.92928571; 0.9248387; 0.80666666; 1.61666666; 0.445; 0.896; 1.16393442; 0.97325949; 1.2182258; 0.37333333; 0.6; 1.205; 1.04; 0.526708; 0.51356; 0.492376; 0.50296; 0.510185; 0.45576051; 0.48071428; 0.76648706; 0.63675998; 0.67993838; 0.75704387; 0.74233194; 0.66765735; 0.68128674; 0.8110722; 1.05716568; 0.92286116; 0.601645; 0.600961; 0.581151; 0.594568; 0.564483; 0.4634708; 0.48405517; 0.45466692; 0.4738303; 0.55965417; 0.57909284; 0.61668961; 0.56378064; 0.64305949; 0.72289567; 0.69039855; 0.70496153; 0.620131; 0.617405; 0.591444; 0.523314; 0.484209; 0.41010752; 0.49441636; 0.67594661; 0.68200748; 0.60590792; 0.57742171; 0.69760102; 0.75706064; 0.76870254; 0.71340283; 0.64351652; 0.59741666; 0.86875; 0.791098; 0.770434; 0.999619; 1.074227; 0.9665; 0.9293924; 1.12669556; 1.06495215; 1.1913263; 1.10882294; 1.27297816; 1.15853917; 1.17612769; 1.09807766; 1.18662638; 1.29825921; 0.79; 0.647; 0.701; 0.753043; 0.753888; 0.7732; 0.72088235; 1.12318181; 1.02873303; 0.98320987; 1.25948905; 1.76197568; 1.67514619; 1.52056; 1.32796774