Time series forecasting is a powerful machine learning method that leverages historical time-stamped data to predict future events and help reduce uncertainty from business conditions — for example, to accurately predict sales, inventory levels, and even manufacturing data.
Much of the data your company has is already time-stamped. It’s probably sitting in Excel spreadsheets, brimming with potential. Here are five ways you could use that data for time series forecasting.
Turn Excel spreadsheets into future knowledge about your business
You’ve been collecting information about your business for years, all stored neatly in an Excel spreadsheet. That data tells the story of where your business has been, but you can also use it to predict what will happen, what demand will look like, the cost of materials, or how shipping times might change. Times series forecasting utilizes time-stamped data — whether that is dates, years, hours, minutes, or seconds — to analyze past temporal patterns and make predictions about the future relevant to your business. If you’re just starting with time series forecasting, new out-of-the-box foundation models let you get started immediately. Foundation models are already pre-trained on large data sets, so during inference, you can directly input your data and quickly see predictions without further training. Options for these foundational models include Nixtla TimeGPT-1, Amazon Chronos, Google TimesFM, Salesforce Moirai, Lag-Llama, and MOMENT. TimeGPT has an Excel plug-in that lets you do the forecasting from within Excel.