You will get time series analysis using ML & deep learning models
Project details
In this project, we aimed to develop a high-accuracy time series analysis model capable of forecasting and identifying patterns. The project focuses on selecting and optimizing algorithms to achieve 100% prediction accuracy, utilizing the most advanced techniques in time series forecasting.
Key Steps Taken:
1. Data Preprocessing: Handle missing values, outliers, and seasonality along with Feature engineering
2. Algorithm Selection: Range of advanced algorithms that are particularly suited for time series data.
- **ARIMA** and **SARIMA** for capturing linear trends and seasonality.
- **Unobserved Component Model** to model the data's level, trend, and seasonality.
- **Prophet** for handling missing data and incorporating holidays or special events.
- **LSTM (Long Short-Term Memory Networks)** for capturing non-linear trends and long-range dependencies, leveraging deep learning for complex patterns.
3. **Model Training & Evaluation:** We will train multiple models on historical data, using cross-validation and performance metrics (RMSE, MAPE, etc.) to evaluate model accuracy and precision.
Key Steps Taken:
1. Data Preprocessing: Handle missing values, outliers, and seasonality along with Feature engineering
2. Algorithm Selection: Range of advanced algorithms that are particularly suited for time series data.
- **ARIMA** and **SARIMA** for capturing linear trends and seasonality.
- **Unobserved Component Model** to model the data's level, trend, and seasonality.
- **Prophet** for handling missing data and incorporating holidays or special events.
- **LSTM (Long Short-Term Memory Networks)** for capturing non-linear trends and long-range dependencies, leveraging deep learning for complex patterns.
3. **Model Training & Evaluation:** We will train multiple models on historical data, using cross-validation and performance metrics (RMSE, MAPE, etc.) to evaluate model accuracy and precision.
Machine Learning Tools
Microsoft Excel, Python, Python Scikit-Learn, RWhat's included
| Service Tiers |
Starter
$50
|
Standard
$75
|
Advanced
$90
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | 2 | 2 | 2 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 4 | 5 | 4 |
Model Validation/Testing | - | - | |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 2 Days)
+$25
Additional Scenario
(+ 1 Day)
+$15
Additional Graph/Chart
(+ 1 Day)
+$20About Pranav
Time series analytics, AI/ML - 14 yrs industry Experience
Ghaziabad, India - 3:48 pm local time
Pharma Life sciences, supply chain, Retail and FMCG who has
worked on:
Platforms-statistical tools like MS Excel, SAS and R
Areas- Category forecasting, Commodity Price forecasting & Supply
chain demand planning, Market mix modeling, time series clustering
Steps for completing your project
After purchasing the project, send requirements so Pranav can start the project.
Delivery time starts when Pranav receives requirements from you.
Pranav works on your project following the steps below.
Revisions may occur after the delivery date.
Data processing, Model validation
Data is processed with handling missing values, outlier treatment Best suited algorithm is implemented to explain the trend and measuring the accuracy basis in sample MAPE, RMSE, etc.