Time Series Analysis

Time Series Forecasting involves using models, trained on historical data, to predict future observations. The accuracy of a Time Series model is determined by its performance in predicting future events. It can be used to predict any sort of data in any industry.

The client faced several issues regarding forecasting. Time Series data is highly complex as it includes large amounts of randomness. The data consists of seasonality and trends that make the data more difficult to analyze. The client wanted to optimize financial predictions as they previously used random guesses for future predictions. Calculated guesses, with respect to past data, were required Once the stationary data is gathered, it is fed to an ARIMA Model, a model used for prediction. Parallel to this, a new pipeline was made where non-preprocessed data was fed to a DL model. Both, the Arima and DL, models were then ensembled and used for prediction. Once the results from both the models were received, they were checked for any similar movements. It is only when both the models predict similar movements, a final decision is taken.

ByteCorp worked closely with the client to use Artificial Intelligence (Al) and Time Series Forecasting to predict, beforehand, future trends in data. The solution allows the client to make future financial decisions in order to maximize profitability and minimize any financial losses.

ByteCorp recommended and implemented an innovative solution based on a Deep Learning (DL) approach, that works towards future predictions of data. This solution uses a 2 pipeline approach where 2 different models are trained, ensembled and then used for predictions. Firstly, the data was visualized to filter out any trends that the data may consist of and to observe how the data behaves. Next, a stationarity test was conducted to observe seasonality in the data. Seasonality was then removed as it affected future predictions. Once seasonality was removed from the data, differencing was done to make the data stationary.

The solution enabled the client to make calculated predictions. The overall risk was minimized and helped the client maximize their profitability and minimize any financial losses.