04. Cherry Blossom Prediction - LSTM vs. Traditional Regression

Abstract: Cherry Blossom Prediction - LSTM vs. Traditional Regression

The enchanting phenomenon of cherry blossoms has captivated cultures across the globe for generations. In this project, we embark on a journey to predict cherry blossom timings using advanced machine learning techniques, specifically focusing on comparing the effectiveness of Long Short Term Memory (LSTM) networks with traditional Regression models.

Our investigation involves an in-depth analysis of historical cherry blossom timing data, encompassing various locations and timeframes. Additionally, we integrate essential meteorological variables such as temperature, humidity, and sunlight duration to enhance prediction accuracy.

A dual approach is adopted:

LSTM Model: Leveraging the power of LSTM, renowned for its ability to capture temporal relationships, we construct a predictive model. Python & R serves as our tool for data preprocessing, feature engineering, and LSTM model development. Through meticulous training and parameter tuning, we harness LSTM's sequence learning capabilities to forecast cherry blossom timings.

Traditional Regression Model: In parallel, we implement a traditional Regression model, leveraging established statistical techniques. This model employs historical cherry blossom timings and meteorological variables as features, predicting cherry blossom timings based on linear relationships between the variables.

The models are rigorously evaluated, comparing their predictive performances using MSE and MAPE metrics with cross-validation, and training/testing split. Beyond predictive accuracy, we delve into interpretability, identifying key features driving each model's predictions. This understanding aids in unraveling the complex relationship between meteorological conditions and cherry blossom timings.

Cherry Blossom Prediction
LSTM (Long Short Term Memory)

Traditional Regression
Machine Learning Techniques

Predictive Model
Data Analysis
Comparison


Predictive Abilities
Data Preprocessing
Feature Engineering


Evaluation Metrics
Predictive Performance
Interpretability


Feature Importance
Statistical Techniques