Seasonal Decomposition
Seasonal decomposition is a tool that breaks down data into three components: trend, seasonality and residual. Trend represents the long-term direction or movement in the data over time. Seasonality represents the repeating short-term patterns or cycles that occur at regular intervals. Further, noise or the residuals represent the random variations or irregular fluctuations in the data that cannot be attributed to trend or seasonality.
Seasonal decomposition improves forecasting by isolating seasonality and trend that makes data easier to model using techniques such as ARIMA or LSTM. Removal of seasonality and trends in the data highlights anomalies, leading to effective noise management. For instance; seasonality affects the trends in the data and complexity arises. Decomposition enables to treat each component separately. Predicatable seasonal fluctuations such as increased retail sales during holidays should be eliminated to draw the true state of market or economic activity.
Differencing and detrending are two commonly used techniques that helps to isolate non-seasonal component of the data to generate clearer understanding of the trends and cycles.
Understanding Customer Insights
Businesses can take advantage of Artificial Intelligence and Machine Learning to study consumer behaviour which is useful to businesses to tailor their products, services and marketing strategies. Individuals can select, purchase, use and dispose the goods and services in different ways, individuals or group of consumers have different ideas or experiences.
Improving data quality and avoiding errors.
Quality of data has a vital role in predictive analytics that shapes the accuracy, reliability and utility of its outcomes. Data quality dimensions encompass accuracy, completeness, consistency, timeliness, reliability, and relevance. Common challenges in ensuring high data quality include human errors in manual data handling, interpretation, and processing, as well as issues related to inadequate and inconsistent data accuracy. The establishment of a robust data governance framework, the formulation of clear data standards, and the regular monitoring of data quality metrics are essential for effective data quality management.
AI and Forecasting Explored
The transformative force of AI can be leveraged into business strategies to enhance decision-making, personalise customer interactions, improve operational efficiencies and foster innovation. Real-time information and analysis of the data, predictive analytics and accurate forecasting enable businesses to enhance decision-making.
Application of Predictive AI Modelling
Real-world applications of predictive AI modelling pose some challenges including data quality, availability, and scalability. Incomplete, noisy and biased data can significantly affect the model’s accuracy and reliability. Scalability of the data or handling of huge volumes of data might require significant computational resources and optimised algorithms.
Exploring ARIMA models...
Real-world data representing the time dimension can be used to forecast future scenarios using time series forecasting methods. Autoregression, moving average, autoregressive moving average and automated machine learning (automated ML) methods are time series forecasting methods which focus on establishing a relationship between historical data and future results. Autoregression assumes the future observations at the next timestamp have a linear relationship with prior time stamps.
Exploring Predictive Analytics
Predictive analytics can help business sustainability by enabling organisations to make data-driven decisions and proactively address potential risks and challenges. By utilizing machine learning algorithms and predictive analytics, businesses can analyse large volumes of data to identify patterns, trends, and potential future outcomes.
