This thesis aims to find a definition of fast-selling clothing options and to find a way to predict them using only a few weeks of sale data as input. The data used for this project contain daily sales and intake quantity for seasonal options, with sale start 2016-2018, provided by the department store chain Åhléns. A definition is found to describe fast-selling clothing options as those having sold a certain percentage of their intake after a fixed number of days. An alternative definition based on cluster affiliation is proven less effective. Two predictive models are tested, the first one being a probabilistic classifier and the second one being a k-nearest neighbor classifier, using the Euclidean distance. The probabilistic model is divided into three steps: transformation, clustering, and classification. The time series are transformed with B-splines to reduce dimensionality, where each time series is represented by a vector with its length and B-spline coefficients. As a tool to improve the quality of the predictions, the B-spline vectors are clustered with a Gaussian mixture model where every cluster is assigned one of the two labels fast-selling or ordinary, thus dividing the clusters into disjoint sets: one containing fast-selling clusters and the other containing ordinary clusters. Lastly, the time series to be predicted are assumed to be Laplace distributed around a B-spline and using the probability distributions provided by the clustering, the posterior probability for each class is used to classify the new observations. In the transformation step, the number of knots for the B-splines are evaluated with cross-validation and the Gaussian mixture models, from the clustering step, are evaluated with the Bayesian information criterion, BIC. The predictive performance of both classifiers is evaluated with accuracy, precision, and recall. The probabilistic model outperforms the k-nearest neighbor model with considerably higher values of accuracy, precision, and recall. The performance of each model is improved by using more data to make the predictions, most prominently with the probabilistic model.

Demand forecasting for sales is a widely researched topic that is essential for a business to prepare for market changes and increase profits. Existing research primarily focus on data that is more suitable for machine learning applications compared to the data accessible to companies lacking prior machine learning experience. This thesis performs demand forecasting on a known sales dataset and a dataset accessed directly from such a company, in the hopes of gaining insights that can help similar companies better utilize machine learning in their business model. LigthGBM, Linear Regression and Random Forest models are used along with several regression error metrics and plots to compare the performance of the two datasets. Both data sets are preprocessed into the same structure based on equivalent features found in each set. The company dataset is determined to be unfit for machine learning forecasting even after preprocessing measures and multiple possible reasons are established. The main contributors are a lack of observations per article and uniformity through the time series.

Idag finns mängder av företag i olika branscher, stora som små, som vill förutsäga sin försäljning. Det kan bland annat bero på att de vill veta hur stort antal produkter de skall köpa in eller tillverka, och även vilka produkter som bör investeras i över andra. Vilka varor som är bra att investera i på kort sikt och vilka som är bra på lång sikt. Tidigare har detta gjorts med intuition och statistik, de flesta vet att skidjackor inte säljer så bra på sommaren, eller att strandprylar inte säljer bra under vintern. Det här är ett simpelt exempel, men hur blir det när komplexiteten ökar, och det finns ett stort antal produkter och butiker? Med hjälp av maskininlärning kan ett sånt här problem hanteras. En maskininlärningsalgoritm appliceras på en tidsserie, som är en datamängd med ett antal ordnade observationer vid olika tidpunkter under en viss tidsperiod. I den här studiens fall är detta försäljning av olika produkter som säljs i olika butiker och försäljningen ska prediceras på månadsbasis. Tidsserien som behandlas är ett dataset från Kaggle.com som kallas för “Predict Future Sales”. Algoritmerna som används i för den här studien för att hantera detta tidsserieproblem är XGBoost, MLP och MLR. XGBoost, MLR och MLP har i tidigare forskning gett bra resultat på liknande problem, där bland annat bilförsäljning, tillgänglighet och efterfrågan på taxibilar och bitcoin-priser legat i fokus. Samtliga algoritmer presterade bra utifrån de evalueringsmått som användes för studierna, och den här studien använder samma evalueringsmått. Algoritmernas prestation beskrivs enligt så kallade evalueringsmått, dessa är R², MAE, RMSE och MSE. Det är dessa mått som används i resultat- och diskussionskapitlen för att beskriva hur väl algoritmerna presterar. Den huvudsakliga forskningsfrågan för studien lyder därför enligt följande: Vilken av algoritmerna MLP, XGBoost och MLR kommer att prestera bäst enligt R², MAE, RMSE och MSE på tidsserien “Predict Future Sales”. Tidsserien behandlas med ett känt tillvägagångssätt inom området som kallas CRISP-DM, där metodens olika steg följs. Dessa steg innebär bland annat dataförståelse, dataförberedelse och modellering. Denna metod är vad som i slutändan leder till resultatet, där resultatet från de olika modellerna som skapats genom CRISP-DM presenteras. I slutändan var det MLP som fick bäst resultat enligt mätvärdena, följt av MLR och XGBoost. MLP fick en RMSE på 0.863, MLR på 1.233 och XGBoost på 1.262 / Today, there are a lot of companies in different industries, large and small, that want to predict their sales. This may be due, among other things, to the fact that they want to know how many products they should buy or manufacture, and also which products should be invested in over others. In the past, this has been done with intuition and statistics. Most people know that ski jackets do not sell so well in the summer, or that beach products do not sell well during the winter. This is a simple example, but what happens when complexity increases, and there are a large number of products and stores? With the help of machine learning, a problem like this can be managed easier. A machine learning algorithm is applied to a time series, which is a set of data with several ordered observations at different times during a certain time period. In the case of this study, it is the sales of different products sold in different stores, and sales are to be predicted on a monthly basis. The time series in question is a dataset from Kaggle.com called "Predict Future Sales". The algorithms used in this study to handle this time series problem are XGBoost, MLP and MLR. XGBoost, MLR and MLP. These have in previous research performed well on similar problems, where, among other things, car sales, availability and demand for taxis and bitcoin prices were in focus. All algorithms performed well based on the evaluation metrics used by the studies, and this study uses the same evaluation metrics. The algorithms' performances are described according to so-called evaluation metrics, these are R², MAE, RMSE and MSE. These measures are used in the results and discussion chapters to describe how well the algorithms perform. The main research question for the study is therefore as follows: Which of the algorithms MLP, XGBoost and MLR will perform best according to R², MAE, RMSE and MSE on the time series "Predict Future Sales". The time series is treated with a known approach called CRISP-DM, where the methods are followed in different steps. These steps include, among other things, data understanding, data preparation and modeling. This method is what ultimately leads to the results, where the results from the various models created by CRISP-DM are presented. In the end, it was the MLP algorithm that got the best results according to the measured values, followed by MLR and XGBoost. MLP got an RMSE of 0.863, MLR of 1,233 and XGBoost of 1,262