Japio’s Forecasting Methodology

Japio's Forecasting Methodology

Patent Number: 11,630,837- Computer Implemented System and Method for Creating Forecast Charts

Japio’s forecasting feature is built on advanced data science principles, offering two powerful models: one using the Prophet and the other using LSTM (Long Short-Term Memory). Our Patented Technology analyzes historical data and uses these models to provide accurate predictions for key metrics, helping businesses anticipate future trends. Developed by Japio’s  Data Science team. You bring the data and we handle the science with our patented technology.

Both models cater to different types of time-series data, ensuring users receive the most accurate forecasts based on their specific data patterns, whether it’s long-term dependencies with LSTM or seasonality with Prophet.

Japio’s forecasting feature is designed to provide highly accurate time series predictions, tailored to marketing and sales data. By combining the strengths of Prophet and  LSTM, Japio ensures that users gain actionable insights to optimize their decision-making.

Model 1: created using Prophet library which has been used in all google analytics dashboards and its respective metric templates.

Model 2: Created for the other dashboards & its respective metric templates, this model has been created using the LSTM library. 

Pre-built Forecasting Charts—no coding required!

Prophet Model

Prophet, developed by Meta, is designed for easy use with business data containing trends and seasonal patterns, making it ideal for Google Analytics or ad performance forecasts.
Key Features:
  1. Trend and Seasonality Detection Prophet automatically detects and projects long-term trends and cyclical patterns (e.g., weekly, monthly).
  2. Handles Missing Data and Outliers: Prophet is robust against data gaps and irregularities, ensuring that predictions remain reliable.
  3. Flexible Growth Models: Supports both linear and logistic growth, making it ideal for businesses scaling rapidly or reaching saturation.
  4. Fast and Efficient Forecasting: Provides quick results, enabling businesses to adjust their strategies in real-time.
  5. Confidence Intervals: Each forecast includes confidence intervals, helping decision-makers prepare for a range of potential outcomes.

How Prophet Works:

Prophet models a time series using three core components:
y(t)=g(t)+s(t)+h(t)+ϵty(t) = g(t) + s(t) + h(t) + \epsilon_ty(t)=g(t)+s(t)+h(t)+ϵt

Where:

  • g(t)g(t)g(t): Trend component (e.g., linear or logistic growth)
  • s(t)s(t)s(t): Seasonal component (e.g., weekly, yearly seasonality)
  • h(t)h(t)h(t): Holiday or event effects
  • ϵt\epsilon_tϵt​: Error term capturing random noise

Accuracy Metrics::

Prophet models are evaluated using Mean Absolute Percentage Error (MAPE), MSE, and RMSE, providing insights into both the direction and magnitude of the prediction errors.

LSTM (Long Short-Term Memory) Model:

LSTM is a powerful neural network model used for time series forecasting. It excels in learning from long-term dependencies, which is essential for marketing metrics like sales projections and ad performance tracking.

Key Features:
  1. Captures Long-Term Dependencies: LSTM models can retain historical information over long periods, providing more accurate forecasts by considering both short-term and long-term data patterns
  2. Customizable for Business Needs: The model can be tailored for different metrics, such as sales, customer lifetime value, and return on ad spend.
  3. Handling Data Irregularities: It processes noisy or incomplete data, making it robust against real-world data challenges.
  4. Real-Time Insights: LSTM enables continuous forecasting updates, allowing agile decision-making during campaigns.

How LSTM Works::

LSTM’s architecture involves gates and memory cells, which regulate the flow of information and ensure only relevant details are retained. Its operation can be summarized with the following formula:

ht=ot×tanh⁡(ft×Ct−1+it×C~t)h_t = o_t \times \tanh(f_t \times C_{t-1} + i_t \times \tilde{C}_t)ht​=ot​×tanh(ft​×Ct−1​+it​×C~t​)

Where:

  • hth_tht​: The hidden state at time t
  • oto_tot​: Output gate
  • ft×Ct−1f_t \times C_{t-1}ft​×Ct−1​: Forget gate and previous memory
  • it×C~ti_t \times \tilde{C}_tit​×C~t​: Input gate and new information
  • tanh⁡\tanhtanh: Activation function

Accuracy Metrics::

LSTM forecasts are evaluated using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), which measure how closely the forecasted values match the actual outcomes. Additionally, Mean Absolute Error (MAE) is used to assess the magnitude of errors without considering their direction.

Data Collection

Japio’s forecasting models retrieve data directly from the respective dataset tied to each metric. These datasets are frequently refreshed to ensure they reflect the latest information from the original data source, providing up-to-date and accurate forecasts. Whether pulling from marketing platforms, CRM systems, or web analytics tools, Japio seamlessly integrates with your data streams, enabling continuous updates. This real-time data collection ensures that our forecasting models are built and updated with the most current data available for more reliable predictions.

Data Preparation

For accurate forecasting, it is essential to have well-formatted and clean data. At Japio, we ensure that the datasets used in our forecasting models are thoroughly preprocessed. This involves handling missing values, removing outliers, and transforming data into a consistent format. Proper data preparation not only enhances the performance of models like LSTM and Prophet but also ensures that the forecasts reflect true patterns and trends. By using high-quality data, Japio provides reliable and actionable insights for your business.

Line Chart in the Dashboard

What is a Line Chart?

A line chart is a visual representation that connects data points with lines, making it ideal for displaying time-series data. In Japio, line charts are used to show how key metrics evolve over time, helping users easily recognize patterns and trends.

Line Charts for Forecasting

Japio offers no-code line charts in dashboards, with just one click using pre-built templates. These charts are crucial for understanding business metrics like sales or customer engagement and are especially effective for analyzing key performance indicators (KPIs).

By visualizing forecasts with line charts, Japio users can clearly observe both historical data and predicted trends, empowering businesses to make informed decisions.

Why Japio’s Forecasting is Accurate and Reliable
  1. Handling Complex Data: Both models are robust in dealing with irregularities, missing data, and outliers, ensuring accurate results even with imperfect datasets.
  2. Real-Time Forecasting: Japio’s system is designed for agility, allowing users to adjust campaigns and strategies based on up-to-date, real-time predictions.
  3. Multiple Time Horizons: Whether short-term (daily, weekly) or long-term (monthly, yearly), Japio’s models are flexible and adapt to various time horizons.

Why Japio’s Forecasting Is Right for Your Business

Japio’s forecasting feature empowers users to make informed decisions with confidence, offering robust tools for any business looking to predict future performance accurately. Whether you’re managing marketing campaigns, optimizing sales funnels, or predicting website traffic, our LSTM and Prophet models deliver top-tier predictive capabilities for your data.

Real-Time Insights: Adjust marketing and business strategies based on up-to-date forecasts.
Confidence Intervals: Prepare for different business outcomes with confidence.
Short and Long-Term Forecasts: Whether for the next week or the next year, Japio adapts to your needs.

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