What is the optimal time window for time series data forecasting?

1 What is a time window?

A time window, also known as a forecasting horizon, is the period of time that you want to forecast based on your data. For example, if you have daily sales data and you want to predict the next week's sales, your time window is seven days. A time window can be fixed or dynamic, depending on whether you use the same or different periods for each forecast. A fixed time window is simpler and more consistent, but a dynamic time window can adapt to changing patterns and trends.

3 How to choose the time window?

Choosing the optimal time window for time series data forecasting is not straightforward, but there are some general steps and tips that can be followed. Firstly, analyze your data to identify its characteristics, such as seasonality, trend, cyclicality, stationarity, autocorrelation, and outliers. This will help you determine the appropriate frequency and granularity of your data. Secondly, define your business problem and forecasting purpose to determine the relevant time horizon and acceptable level of accuracy. Thirdly, experiment with different time windows and evaluate their performance using metrics such as MAE, RMSE, MAPE or sMAPE. Cross-validation or backtesting techniques can also be used to evaluate how well your forecasts generalize to new data. Lastly, choose a suitable model for your data and objectives such as exponential smoothing, ARIMA or neural networks. Alternatively, ensemble methods or hybrid models can be used to combine different models and improve forecasts. Python libraries such as scikit-learn, statsmodels or prophet can be used to implement and optimize models.

5 What are some examples of time windows?

Time windows for different time series data forecasting scenarios can vary depending on the data and the objectives. For instance, if you want to forecast monthly sales of a retail store, you may use a time window of 12 months to capture the seasonality and the trend of your data. Alternatively, if you need to forecast daily sales of a specific product, a shorter time window of 7 days should be used to account for the variability and the demand fluctuations. When forecasting future stock prices, a time window of 1 day can capture the short-term movements and the volatility of your data. On the other hand, if you want to forecast long-term returns of a portfolio, a longer time window of 1 year should be used to capture the risk and the diversification of your data. Similarly, if you need to forecast hourly electricity consumption of a household, you may use a time window of 24 hours to capture the diurnal cycle and the weather effects. But if you want to forecast annual energy consumption of a country, a longer time window of 10 years should be employed to capture the economic and demographic trends.