Sales Forecasting

Keywords: Time Series Forecasting, Sales Prediction, Manufacturing Industry, Supply Chain Optimization, Demand Planning

Sales forecasts are indispensable for companies, especially in the manufacturing industry. They enable companies to make well-founded decisions. They therefore often serve as the basis for planning processes along the supply chain. Sales forecasts help to plan production capacities, optimize stock levels and avoid supply bottlenecks. To avoid planning errors, the accuracy of sales forecasts is of great importance. However, demand patterns can be very complex. They depend on various factors, such as the time series pattern, e.g. trend, seasonal or sporadic, the company’s own marketing or the current purchasing power of customers.

In addition, nowadays more and more data is available for creating forecasts. Machine learning algorithms are able to generate precise forecasts from large volumes of data.

The aim of this challenge is to calculate a sales forecast for a company using machine learning methods. The dataset provides information about the sales of a production company located in the Ruhr area in Germany. The training data contain the documentation of sales from the 1st December 2012 until 30.06.2018 of five products. The task is to create monthly sales forecasts for July, August and September of 2018. To forecast, please divide the training data into proper training data, validation data and test data.

To improve your sales prediction, we suggest to previously apply cluster methods to group the time series.

https://www.kaggle.com/competitions/sales-forecasting-new/data

Sample from the data

Date	X1	X2	X3	X4	X5	X6	X7	Sales Quantity	Sales Unit
01.12.2012	P1	S1	D1	O1	MD1	MG1	MC1	7992.0	PC
06.12.2012	P2	S1	D1	O2	MD3	MG2	MC2	79923.0	PC
06.12.2012	P3	S1	D1	O3	MD4	MG3	MC3	287724.0	M
02.12.2012	P2	S1	D1	O4	MD5	MG4	MC4	47954.0	PC
08.12.2012	P4	S1	D2	O5	MD3	MG5	MC5	47954.0	PC

Description

Variable	Type	Values	Description
Date	Datetime	Dec 2012 to June 2018	Date/time of sale (shifted for privacy)
X7	Categorical	MC1–MC2032	Product identifier
X2 – X6	Categorical	X1: P1-P5 X2: S1–S6 X3: D1–D13 X4: O1–O53 X5: MD1–MD5 X6: MG1–MG208	Hierarchical anonymized product features: • X7 represents the exact product designation. • Each value of X7 maps uniquely to one value of X6. • Each value of X6 maps uniquely to one value of X5. • X4 represents subcategories of X2.
SalesQuantity	Numerical	376.0 to 500,000,000.0	Number of units sold (may be transformed)
SalesUnit	Categorical	PC (piece) M (meter) M² (square meter) KG (kg)	Unit of sale

2. A sample from your dataset also helps to visualize the challenge

3. A table of the description (variables, definitions, datatype)

4. License agreement

5. Some further comments if necessary

Evaluation method

$$ \mathrm{RMSE} := \sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_i – \hat{y}_i\right)^2} $$

where yᵢ is the actual value and ŷᵢ is the predicted value.

Please additionally report the RMSE on your training data.

Submission Format: Please submit a csv file containing the following variables in this exact order:
Date, X1, Forecast, SalesUnit

Micro lectures: The Micro Lectures offer a comprehensive insight into various topics in order to convey the most important information in a compact format. Each Micro Lecture focuses on a specific topic:

• Installing Python
• Introduction to ML
• Data Visualization
• Preparation
• Clustering
• Feature Extraction
• Time Series Forecasting
• Forecasting Methods
• Hyperparameter Tuning
• Overfitting
• Imbalanced Datasets
• Random Forest
• XGBoost
• LightGBM
• Evaluation metrics