Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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A Review on Linear Regression Comprehensive in Machine Learning

Sales forecasting is a multivariate time series forecasting problem. The main challenges of the forecasting task are the high-dimensional influence variables with noise and the complex time series relationships. In this paper, we propose a DAE-LSTM algorithm combined with denoising autoencoder(DAE) and long short-term memory(LSTM) to deal with this problem. The DAE is a non-linear dimensionality reduction method that enhances data robustness. The LSTM is a deep learning algorithm suitable for dealing with multivariate time series problem. Combining the advantages of the above two algorithms, DAE-LSTM is a new multivariate time series prediction algorithm, which transforms unsupervised DAE into supervised data feature extraction method, and adds feature selection function to LSTM algorithm. In addition, in order to build a comprehensive forecasting framework, the hyperparameters of the model are determined by Bayesian optimization method(BOM) and the parameters of the model are solved by adaptive moment estimation(ADAM) optimization algorithm. In the training process, due to the adaptability of the BOM and ADAM, the forecasting model has few parameters that need to be set artificially, only including dimensional scale and time steps determined by data. Finally, the proposed forecasting framework is applied to forecast the sales of online retailers on JD.COM. In comparison with other machine learning algorithms, including multiple linear regression(MLR), support vector regression(SVR), artificial neural network(ANN) and LSTM, the proposed DAE-LSTM achieves the best prediction accuracy.

https://iopscience.iop.org/article/10.1088/1742-6596/1746/1/012087/pdf

Optimization of a Comprehensive Sequence Forecasting Framework Based on DAE-LSTM Algorithm

Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society’s production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules. 

/pdf/a-new-apache-spark-based-framework-for-big-data-streaming-1prvwyum.pdf

A new Apache Spark-based framework for big data streaming forecasting in IoT networks

Sales prediction with minute accuracy plays a crucial role for an organization to sustain amidst the global competitive business environment. The use of artificial intelligence (AI) on top of the existing information technology environment has become one of the most exciting and promising area for any organizations in the current era of digital marketing. E-marketing provides customers to share their views with other customers. In this paper, the authors proposed a model which will be helpful to the digital marketers to find out the potential customers to extract value from customer feedback. The proposed model is based on artificial neural network and will make it possible to identify the customer demand depending on previous feedback and to predict the future sales volume of the product. The authors tried to utilize AI, mainly neural networks (NNs), to construct an intelligent sales prediction and also to apply ANNs for prediction regarding sales of mobile phone (Redmi, Note 6 Pro) one month ahead depending on customer feedback on two e-commerce platform, namely Amazon.in and Snapdeal.in.

/pdf/ai-based-sales-forecasting-model-for-digital-marketing-3jj9l4ai.pdf

AI-Based Sales Forecasting Model for Digital Marketing

: Sales predictions or forecasting can help in analyzing the current and future sales trends of a big mart company. Based on the sales prediction or forecast, a retailer company can plan its production, marketing and promotional activities. Using several machine learning techniques, the obtained data may then be utilized to predict possible sales for retailers. This paper investigates that which machine learning regression algorithm best predicts big marts sales and which technique has the highest correlation coefficient value and the lowest values of mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), and root relative squared error (RRSE). A comparative analysis of various machine learning regression algorithms such as SMO regression, simple linear regression, linear regression, additive regression, multi-layer perceptron, random forest, and M5P will be provided in this paper. After the experiments are completed, a comparison of various cross validations and splitting ratios for training and testing data will be given.

Comparative Analysis of Regression Algosrithms used to Predict the Sales of Big Marts

The aim of this paper is to analyze the sales of a big superstore, and predict their future sales for helping them to increase their profits and make their brand even better and competitive as per the market trends by generating customer satisfaction as well. The technique used for prediction of sales is the Linear Regression Algorithm, which is a famous algorithm in the field of Machine Learning. The sales data is from the year 2011-13 and prediction of data for the year 2014 is done. Then, real-time data of the year 2014 is also taken and the actual data of the year 2014 has been compared to the predicted data to calculate the accuracy of prediction. This is done so as to validate our results with the actual ones. This in turn would help them take necessary actions (which has been discussed later) for their increase their sales.

Gopalakrishnan T

Papers

Prediction of Sales Value in Online shopping using Linear Regression