Abstract:
Time series Analysis (TSA) is used to explore, analyze and predict time
series data, and its ultimate goal is to predict future values based on historical
data. Sales forecasting plays a vital role in business management; it is one of
the most common research areas in time series data analysis. Revenue
prediction based on sales forecasting is a critical processes in a business
because so many areas of business are integrated into it. Most companies
forecast their future sales revenue in the coming season/yearly wise to
take financial decisions, expand/minimize their business and supply
chains, employee hiring process, advertising & marketing, and many
more. Accurate forecasting is crucial for a sales company for decisionmaking;
inaccurate forecasting negatively impacts the company in the
short or long term. Sales forecasts help to achieve target sales/revenue by
identifying early warnings in their sales pipeline and mitigating the loss
of revenue and its risk. According to the literature, sales forecasting
performs based on the regional, country, and seasonal-wise. In this paper
has used different algorithms (RNN, Random Forest, XGBoost, LSTM and
proposed CNN-LSTM model) to compare weekly, monthly and quarterly
sales prediction accuracies. For the implementation, Tensorflow has used
with the computational support of GPU. Minimum MSE observed in
proposed model for weekly sales; i.e mse = 19.18. By applying the CNN
layer to the existing LSTM model focused to identify the hidden patterns in
multivariate time series data. So compared to the above mentioned models,
the CNN- LSTM model performed well and minimizes the MSE value.
