Introduction-to-Time-Series-Analysis.pptx

About time series analysis

1. QUANTITATIVE TECHNIQUES TOPIC : Time Series Analysis PRESENTED TO : Dr. Asha Mishra PRESENTED BY : Parth Atre MBA 1 SEMESTER SECTION : B

2. Introduction to Time Series Analysis Time series analysis examines data points in time order. It helps understand past behavior and forecast future values. Common applications include finance, economics, and marketing. This presentation will cover core concepts, data preprocessing, and forecasting techniques.

3. Core Concepts: Components of a Time Series Trend Long-term direction (upward, downward, or flat). For example, increasing EV sales. Seasonality Repeating patterns within a fixed period. Retail sales peak during the holidays. Cyclicality Longer-term patterns not of fixed frequency. Consider economic recessions. Irregularity Random, unpredictable variations, for example, a sudden demand spike.

4. Data Preprocessing for Time Series Missing Values Impute using mean, median, or interpolation. Replace missing temperatures with interpolated values. Outlier Treatment Identify and handle extreme values. Remove abnormally high sales from a promotion. Smoothing Reduce noise to reveal patterns. Use a 7-day moving average for stock prices. Stationarity Making the time series' mean and variance constant over time.

5. Stationarity and Differencing 1 Definition Stationary time series have constant statistical properties. Mean and variance do not change over time. 2 Importance Most time series models assume stationarity. Achieving stationarity is crucial for accurate modeling. 3 Testing Use ADF or KPSS tests. Determine if a stock price series is stationary using the ADF test. 4 Differencing Transform non-stationary series by subtracting consecutive observations.

6. Common Time Series Models: ARIMA p Order of autoregression (AR). Uses past values to predict future values. 1 d Order of integration (I). Differencing passes required for stationarity. 2 q Order of moving average (MA). Uses past forecast errors. 3 ARIMA models define three main components (p, d, q). Model selection uses ACF and PACF plots. Model evaluation uses AIC and BIC.

7. Time Series Forecasting Techniques Exponential Smoothing Suitable for series with trend or seasonality. State Space Models Kalman Filters for linear time-varying systems. Machine Learning Regression, Random Forests, and Gradient Boosting. Feature engineering. Various techniques exist for forecasting. These include Exponential Smoothing and Machine Learning.

8. Evaluating Forecast Accuracy Metrics MAE, MSE, RMSE, MAPE. Calculate metrics for a sales forecast. Visual Inspection Plot forecasts against actual values. Compare forecasted and actual stock prices. Residual Analysis Check for patterns in the forecast errors. Ensure residuals are random.

9. Practical Considerations and Conclusion 1 Data Quality Clean and reliable data is key to achieve better data analysis. 2 Model Selection Choose a model for specific data and goals. 3 Domain Knowledge Incorporate domain expertise.