The MSP Finance Team Financial Forecasting app enables users to make informed decisions on resource allocation, investments, budgeting, risk management, and strategic planning. By analyzing historical data, trends, and seasonality, you can plot potential future outcomes and inform decision-makers of the direction of the company's finances.
This article discusses the following about MSP Finance Team Financial Forecasting:
The MSP Finance Team Financial Forecasting app of MSPbots uses Overall Trends and Seasonality to calculate values for forecasting. For example, the following seasonality graph illustrates how it is possible to forecast the high price seasonality of commodities corn and soy, where abundant supply during the fall harvest period results in lower prices. Based on the values in the graph, this period reaches its peak during the summer months.
Seasonality Graph
The following graphs show how time components affect trends.
Components of Time Series
A. Trend B. With Cyclical Values
Fig. A displays a simple trend line of value (Y) over time (X) Fig. B Trend line overlaid with cyclical (regularly recurring) movement
C. With Cyclical and Seasonal Variations D. With Cyclical and Seasonal Variations and Random fluctuations
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Figures C and D add Seasonal and Random value movements to the trend.
MSPbots uses a Machine Learning model to automatically calculate and detect Overall Trends and Seasonality.
This model uses the following methods to forecast values:
Because we apply the Machine Learning model, the forecasted values get better as more data becomes available for calculation and analysis. Ideally, data from one to two years is sufficient to achieve an acceptable level of accuracy and error margin.
Below are examples of forecasts available in the app.
The calculations for this Model is run based on an open-source code granted through this license.