In a data-driven world, identifying trends gives an authoritative view of the future. Trend analysis is a fundamental method to discover patterns in historical data that give way to new opportunities. A trend is a general direction in which the variables of interest are moving for a specific period of time. The longer the direction, the more notable is the trend. Isolating an event from these movements are complex in themselves as they are influenced by several factors.
Putting time-series data to work
One of the significant concern is prioritizing current resources for the future. Trend analysis is a technique that can help to determine patterns in historical data and plan for the future.
In a time series, the data consists of systematic patterns and random noise that makes identifying patterns difficult. These patterns can be either be seasonal or trend depending upon their occurrences.
Comparing an expected trend with the identified trend helps to identify gaps and plan to bridge them. Most real-world time series data consists of outliers or extreme fluctuations due to rare events.
Once the basic tenets are understood, successful time series models can be constructed. Consider a model that is meaningful and accurate and produces independent residuals.
There will be historical assumption errors when the forecasts play out differently. Incorporating a sum of the assumption errors and model error can help to understand the most impactful drivers of the process.
Determine an optimal future with trend analysis
With our expertise and experience in the application of different methods, you get an outside view of what your competitors are doing to stay relevant and competitive. Our proven ability and expertise of over 10 years will help to estimate the degree of impact of a pitfall and probability of re-occurrence of the event.
Stock market trend analysis or equity market trend analysis refers to the process of examining... More
In the previous article, all possibilities for performing Autoregressive Integrated Moving Average (ARIMA) modeling for... More
After performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modeling for time series... More
Missing data is one of the most common problems in almost all statistical analyses. If... More
The previous article showed that the three-time series values Gross Domestic Product (GDP), Gross Fixed... More
Numerous challenges can derail strategies and every method to strategise is unique to its goals. Our goal with trend analysis is to find out why and how a change in variables occurs in a range of time. The aim is to provide an insightful analysis of the changing dynamics of competitions to stay ahead.