time series analysis

Introducing time series analysis

Time series analysis is used to estimate how a sequence of observations will continue. Research is focused on changes of values over time. More companies are discovering or already using forecasting (and other predictive techniques) effectively. For business decisions the time series patterns are interesting like trends, seasonality and business cycle. A trend is variable of financial and economic results caused by growth, regression, peaks and recovery of businesses.

Forecasts are an important element in the development of time series analysis for business and why they are needed throughout an organization continually. Every company is making strategic decisions under some level of uncertainty. We are not always aware of the fact that we are forecasting, but choices are directed by our anticipation of results. This can be with our actions or the consequences of inactions. A business that makes a critical call on the right moment is ahead of competitors and works efficient and decisively. Neither is forecasting ever finished. As time moves on the impact of forecasts on actual performance is measured, original forecasts are updated, decisions are modified, and so on.

Relation with artificial intelligence (AI) and machine learning

Time series forecasting is based on AI and machine learning techniques and can be used to create a model that predict sales (predictive forecast model). Besides AI and machine learning and math, econometrics and logic strengthen the possibilities for a data scientist to develop advanced forecasting methods.

Right now we will keep working on interesting time series analysis, development, implementation, and further optimization. In the near future, I’ll write a short blog post about the time series analysis we did for one of our clients, RB, who wanted over 90% accuracy with their sales prediction. Quite a challenge!


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