Time Series and Forecasting

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If you wanted to predict the price of your favorite stock tomorrow you could do so by looking at patterns over the past several days, months or years. You might also incorporate economic or political factors. Time series methods seek to model expected levels of variables given past behavior and more sophisticated methods can incorporate other independent variables. Companies have numerous applications for forecasting such as call volume, sales volume, inventory levels, resource usage, expected material costs, and so on. Like most applied statistics, there a simple models and there are also highly complex approaches depending on the application.

Example areas of Adsurgo expertise in time series include:

  • Smoothing methods
  • Autoregressive Integrated Moving Average Models (ARIMA)
  • Seasonal modeling
  • Autoregressive modeling with covariates
  • Transfer function models
  • Doubly stochastic methods with generalized linear mixed models
  • Econometric models with panel data and instrument variables

Let Adsurgo help you to minimize your forecast error so you can improve business performance with accurately projected future values.