Time Series Forecasting

A statistical method that analyses historical sales data to identify patterns and trends, used to predict future demand.

What is Time Series Forecasting

Time series forecasting is a statistical technique used in retail to predict future data points based on historical time-ordered data. It involves analysing patterns and trends in time series data, which consists of observations collected at discrete, equally spaced time intervals. Retailers often employ time series forecasting to make informed decisions about inventory management, demand planning, sales projections, and resource allocation.

How Time Series Forecasting works

  • Data Collection: Retailers gather historical data on a specific metric, such as sales, inventory levels, or customer foot traffic, collected over regular time intervals (e.g., daily, weekly, monthly).

  • Data Preprocessing: The data is cleaned and preprocessed to remove outliers, fill in missing values, and ensure data consistency. This step is crucial for accurate forecasting.

  • Exploratory Data Analysis (EDA): Retailers conduct EDA to understand the data's characteristics, identify trends, seasonality, and other patterns. This informs the choice of forecasting methods.

  • Model Selection: Based on the EDA findings, retailers select an appropriate time series forecasting model, such as ARIMA (AutoRegressive Integrated Moving Average), Exponential Smoothing, or machine learning techniques like neural networks or Prophet.

  • Training the Model: The chosen model is trained using the historical data, and parameters are optimised to fit the data. This allows the model to capture the underlying patterns and relationships.

  • Validation and Testing: The model's accuracy is evaluated using validation techniques, such as cross-validation, and tested on out-of-sample data to assess its predictive performance.

  • Forecasting: Once validated, the model is used to make future predictions. Retailers can generate forecasts for a specific time horizon, which can range from days to years, depending on the business's needs.

  • Post-Forecasting Analysis: Retailers compare the forecasts to actual outcomes to assess the model's accuracy. Regular updates and retraining of the model may be required to account for changing patterns and data dynamics.

  • Decision-Making: The forecasted results are used to inform retail decisions, such as inventory management, demand planning, pricing strategies, and resource allocation. Accurate forecasts help retailers optimise operations and better serve customers.
Time series forecasting is a valuable tool for retailers seeking to make data-driven decisions, reduce uncertainty, and improve the efficiency of their operations.

Pros of Time Series Forecasting

  1. Improved Inventory Management: Time series forecasting enables retailers to make more accurate predictions of future demand for products. This leads to better inventory management, reducing the risk of overstocking or stockouts, minimising holding costs, and improving overall supply chain efficiency.
  2. Enhanced Sales and Revenue Projections: Retailers can use time series forecasting to project future sales trends accurately. This information helps in developing effective sales and marketing strategies, setting realistic sales targets, and maximising revenue opportunities.
  3. Optimised Resource Allocation: By having insights into future demand patterns, retailers can allocate resources more efficiently. This includes staffing levels, store space utilisation, and marketing budget allocation, resulting in cost savings and improved resource utilisation.

Cons of Time Series Forecasting

  1. Data Quality and Accuracy: Time series forecasting heavily relies on historical data. Inaccurate or incomplete data can lead to unreliable forecasts. Maintaining high-quality data and addressing issues like missing values, outliers, and seasonality is crucial but can be resource-intensive.
  2. Complexity of Model Selection: Selecting the right forecasting model can be challenging. Different models may be suitable for different types of data and patterns, and choosing the wrong model can lead to inaccurate predictions. Retailers need expertise in time series analysis to make the best model selection.
  3. Uncertainty and External Factors: Time series forecasting models may not fully account for external factors, such as economic changes, market trends, or unforeseen events (e.g., a global pandemic). These external factors can introduce uncertainty and make it difficult to predict future outcomes accurately.


Below you will find answers to common questions
How can time series forecasting help us with demand planning and inventory management in our retail business?
Time series forecasting plays a crucial role in demand planning and inventory management. By analysing historical sales data and identifying trends, seasonality, and cyclical patterns, retailers can make informed predictions about future product demand. These forecasts guide inventory stocking levels, ensuring that the right amount of stock is available to meet customer needs without overstocking, reducing carrying costs, and minimising the risk of stockouts.
What are the best practices for selecting and implementing time series forecasting models to improve our sales projections?
Selecting and implementing time series forecasting models requires careful consideration. Retailers should start by understanding their data and the patterns it exhibits. Choosing the appropriate model depends on factors such as the data's stationarity, seasonality, and other characteristics. Implementing best practices, including regular model evaluation and retraining to adapt to changing data patterns, ensures the continued accuracy of sales projections. Investing in forecasting software or working with data scientists can also aid in model selection and implementation.