Time Series Analysis

Marketways Arabia works at the intersection of machine learning, econometrics, and artificial intelligence. We specialize in empowering companies and organisations based in and around Dubai, Abu Dhabi & Riyadh with cutting-edge time series analysis techniques to unlock actionable insights and drive informed decision-making.

In today’s rapidly evolving landscape, accurate trend predictions and forecasting are essential for staying ahead of the curve. That’s where our expertise in time series analysis comes into play. We offer a comprehensive suite of services tailored to meet your specific needs, leveraging state-of-the-art methodologies and models.

Our arsenal includes a wide array of time series models, each designed to tackle unique challenges and extract valuable insights from your data:

Autoregressive (AR) Model: Capture dependencies within your data by modeling time series as a linear combination of past values, ideal for stationary data.

Moving Average (MA) Model: Uncover underlying patterns by modeling series as a linear combination of past forecast errors, suitable for stationary data.

Autoregressive Integrated Moving Average (ARIMA) Model: Handle non-stationary data with ease by combining AR and MA concepts along with differencing.

Seasonal Autoregressive Integrated Moving-Average (SARIMA) Model: Gain deeper insights into seasonal patterns with an extension of ARIMA.

Seasonal Decomposition of Time Series (STL): Understand the underlying structure of your data by decomposing it into seasonal, trend, and residual components.

Exponential Smoothing (ETS) Models: Capture trend, seasonality, and error components with recursive updates based on new observations.

Vector Autoregression (VAR) Model: Extend your analysis to multiple time series variables, modeling each as a linear combination of lagged values.

Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX): Incorporate external factors that influence your time series for more accurate predictions.

Long Short-Term Memory (LSTM) Networks: Harness the power of deep learning to model complex dependencies, especially in large and non-linear datasets.

At the heart of our methodology lies the state space representation, a versatile framework for modeling time series data. By separating the underlying dynamics of the system from observed measurements, we unlock deeper insights and enhance predictive accuracy.

Our team of experts is well-versed in the latest advancements in state space models, including the Kalman filter and its extensions like the extended Kalman filter (EKF) and unscented Kalman filter (UKF). We also leverage particle filters for non-parametric state estimation in nonlinear and non-Gaussian systems.

Whether you’re looking to optimize inventory management, improve sales forecasting, or enhance risk assessment, we’re here to help. Contact us today to discover how our time series analysis expertise can drive success for your business. Let’s transform your data into actionable insights together.

The Information Highway to your Market!

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