Time-Series-Analysis

Analyzing a time series of data can provide insights into its performance over time, including trends, seasonality, and other patterns. Here's a general outline of steps you can take for time series analysis of Amazon:

Data Collection:

• Obtain historical data for Amazon's,Google's and Ford's key performance metrics such as sales revenue, stock prices, number of orders, etc. This data can be collected from various sources including financial databases, stock exchanges, or directly from Amazon's,Google's and Ford's financial reports.

Data Preprocessing:

• Clean the data by handling missing values, removing outliers, and ensuring uniformity in the time intervals (if applicable). Convert the data into a time series format with a timestamp index.

Exploratory Data Analysis (EDA):

• Visualize the time series data to understand its basic characteristics, such as trend, seasonality, and any outliers. Use statistical measures such as mean, median, variance, and autocorrelation to summarize the data.

Decomposition:

• Decompose the time series into its trend, seasonal, and residual components using methods like additive or multiplicative decomposition. This helps in understanding the underlying patterns and identifying any anomalies.

Modeling:

• Choose an appropriate time series forecasting model based on the characteristics of the data. Common models include ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), Exponential Smoothing methods, and machine learning models like LSTM (Long Short-Term Memory).

Forecasting:

• Use the fitted model to make predictions for future time periods. Visualize the forecasted values along with confidence intervals to assess the uncertainty in the predictions.