time-series-analysis

## What I do - Analyze temporal data patterns - Build forecasting models - Detect trends and seasonality - Handle temporal dependencies - Create time series features - Evaluate forecast accuracy ## When to use me Use me when: - Predicting future values - Analyzing trends over time - Detecting seasonality - Anomaly detection in time series - Resource planning and forecasting ## Key Concepts ### Time Series Components ```python import pandas as pd import numpy as np from statsmodels.tsa.seasonal import seasonal_decompose # Decomposition decomposition = seasonal_decompose(ts, model='additive', period=12) decomposition.plot() plt.show() # Components: # - Trend: Long-term direction # - Seasonality: Repeating patterns # - Cyclical: Longer-term oscillations # - Residual: Random noise ``` ### Forecasting Models ```python from statsmodels.tsa.arima.model import ARIMA from statsmodels.tsa.statespace.sarimax import SARIMAX from prophet import Prophet # ARIMA model = ARIMA(train, order=(1, 1, 1)) fitted = model.fit() forecast = fitted.forecast(steps=30) # SARIMA (with seasonality) model = SARIMAX(train, order=(1,1,1), seasonal_order=(1,1,1,12)) fitted = model.fit() # Prophet (Facebook) model = Prophet(yearly_seasonality=True, weekly_seasonality=True) model.fit(df[['ds', 'y']]) future = model.make_future_dataframe(periods=30) forecast = model.predict(future) ``` ### Feature Engineering ```python def create_time_features(df): df['year'] = df['date'].dt.year df['month'] = df['date'].dt.month df['day'] = df['date'].dt.day df['dayofweek'] = df['date'].dt.dayofweek df['quarter'] = df['date'].dt.quarter df['is_weekend'] = df['date'].dt.dayofweek >= 5 # Lag features for lag in [1, 7, 14, 30]: df[f'lag_{lag}'] = df['value'].shift(lag) # Rolling features df['rolling_mean_7'] = df['value'].rolling(7).mean() df['rolling_std_7'] = df['value'].rolling(7).std() return df ``` ### Evaluation Metrics - MAE (Mean Absolute Error) - RMSE (Root Mean Square Error) - MAPE (Mean Absolute Percentage Error) - MASE (Mean Absolute Scaled Error)